Search Results (506)

This paper presents a compact mixed-mode first-order universal filter based on a single voltage differencing current conveyor (VDCC), which can function in all four possible operation modes, i.e., voltage mode (VM), trans-admittance mode (TAM), current mode (CM), and trans-impedance mode (TIM). The proposed configuration requires only two grounded resistors and one floating capacitor, which contributes to a low component count, facilitates integration, and allows for the electronic tunability of the pole frequency through the transconductance gain of the VDCC. This work also demonstrates two practical biomedical applications: an electrocardiogram (ECG) acquisition system utilizing the VM low-pass filter for noise suppression and a bioimpedance (BioZ) measurement system employing the proposed configuration-based CM oscillator circuit as a sinusoidal excitation source. The performance validation confirms the accuracy of impedance extraction and the preservation of waveforms using tissue-equivalent models. The results demonstrate that the proposed VDCC-based filter offers a compact, power-efficient, and versatile analog signal-processing solution suitable for modern biomedical instrumentation. Full article

Electroencephalography-based motor imagery (EEG-MI) classification is a cornerstone of Brain–Computer Interface (BCI) systems, enabling the identification of motor intentions by decoding neural patterns within EEG signals. However, conventional methods, predominantly reliant on convolutional neural networks (CNNs), are proficient at extracting local temporal features but struggle to capture long-range dependencies and global contextual information. To address this limitation, we propose a Triple-path Heterogeneous Feature Collaboration Network (TPHFC-Net), which synergistically integrates three distinct temporal modeling pathways: a multi-scale Temporal Convolutional Network (TCN) to capture fine-grained local dynamics, a Transformer branch to model global dependencies via multi-head self-attention, and a Long Short-Term Memory (LSTM) network to track sequential state evolution. These heterogeneous features are subsequently fused adaptively by a dynamic gating mechanism. In addition, the model’s robustness and discriminative power are further augmented by a lightweight front-end denoising diffusion model for enhanced noisy feature representation and a back-end prototype attention mechanism to bolster the inter-class separability of non-stationary EEG features. Extensive experiments on the BCI Competition IV-2a and IV-2b datasets validate the superiority of the proposed model, achieving mean classification accuracies of 82.45% and 89.49%, respectively, on the subject-dependent MI task and significantly outperforming existing mainstream baselines. Full article

This systematic review examines recent advances in blockchain-based electronic voting systems, motivated by the need for more transparent, secure, and verifiable electoral processes. The rapid growth of research between 2022 and 2025 highlights blockchain as a promising foundation for addressing long-standing challenges of integrity, anonymity, and trust in digital elections, particularly in academic contexts where pilot deployments are more feasible. The review followed PRISMA 2020 guidelines and applied the evidence-based methodology proposed by Kitchenham & Charters. Searches were conducted in six major databases, yielding 861 records; after removing duplicates and applying eligibility criteria, 338 studies were retained. Data were extracted using a structured template and synthesised qualitatively due to the conceptual and methodological heterogeneity of the evidence. The included studies reveal significant progress in blockchain architectures, smart contracts, and advanced cryptographic mechanisms—such as blind signatures, zero-knowledge proofs, and homomorphic encryption. Multiple authentication and verification strategies were identified; however, real-world validations remain limited and largely confined to small-scale academic pilots. Overall, blockchain-based voting systems demonstrate conceptual advantages over traditional and conventional electronic models, especially regarding transparency and auditability. Nevertheless, the field requires stronger empirical evaluation, greater scalability, and clearer regulatory alignment to support broader institutional adoption. Full article

With the increasing application of unmanned aerial vehicle (UAV) in multiple fields, the path planning problem has become a key challenge in the optimization domain. This paper proposes an Improved Artificial Lemming Algorithm (IALA), which incorporates three strategies: the optimal information retention strategy based on individual historical memory, the hybrid search strategy based on differential evolution operators, and the local refined search strategy based on directed neighborhood perturbation. These strategies are designed to enhance the algorithm’s global exploration and local exploitation capabilities in tackling complex optimization problems. Subsequently, comparative experiments are conducted on the CEC2017 benchmark suite across three dimensions (30D, 50D, and 100D) against eight state-of-the-art algorithms proposed in recent years, including SBOA and DBO. The results demonstrate that IALA achieves superior performance across multiple metrics, ranking first in both the Wilcoxon rank-sum test and the Friedman ranking test. Analyses of convergence curves and data distributions further verify its excellent optimization performance and robustness. Finally, IALA and the comparative algorithms are applied to eight 3D UAV path planning scenarios and two amphibious UAV path planning models. In the independent repeated experiments across the eight scenarios, IALA attains the optimal performance 13 times in terms of the two metrics, Mean and Std. It also ranks first in the Monte Carlo experiments for the two amphibious UAV path planning models. Full article

Online signature verification (OSV) is a challenging problem in behavioral biometrics, especially when skilled forgeries closely mimic genuine signatures in both appearance and dynamics. This study presents a multi-class OSV framework that combines hybrid statistical features and nonlinear dimensionality reduction using Uniform Manifold Approximation and Projection (UMAP). A 40-dimensional feature set is created from statistical moments of dynamic writing parameters in both time and frequency (DCT) domains. Experimental results show that UMAP-based dimensionality reduction preserves category-related structures in a compact two-dimensional space. The proposed approach achieves an average classification accuracy of 0.989 ± 0.005 and a Cohen’s kappa coefficient of 0.985 ± 0.006, demonstrating robust performance across multiple classifiers. The results validate the effectiveness of combining multi-domain statistical feature fusion with UMAP for multi-class online signature verification, providing both high performance and interpretable visual representations. Full article

The exponential growth and complexity of legal agreements pose significant Big Data challenges and strategic risks for modern organizations, often overwhelming traditional, manual contract management workflows. While AI has enhanced legal research, most current applications require extensive domain-specific fine-tuning or substantial annotated data, and Large Language Models (LLMs) remain susceptible to hallucination risk. This paper presents an AI-based Agreement Management System that addresses this methodological gap and scale. The system integrates a Python 3.1.2/MySQL 9.4.0-backed centralized repository for multi-format document ingestion, a role-based Collaboration and Access Control module, and a core AI Functions module. The core contribution lies in the AI module, which leverages zero-shot learning with OpenAI’s GPT-4o and structured prompt chaining to perform advanced contractual analysis without domain-specific fine-tuning. Key functions include automated metadata extraction, executive summarization, red-flag clause detection, and a novel feature for natural-language contract modification. This approach overcomes the cost and complexity of training proprietary models, democratizing legal insight and significantly reducing operational overhead. The system was validated through real-world testing at a leading industry partner, demonstrating its effectiveness as a scalable and secure foundation for managing the high volume of legal data. This work establishes a robust proof-of-concept for future enterprise-grade enhancements, including workflow automation and predictive analytics. Full article

As artificial intelligence (AI) becomes increasingly integrated into organizational processes to enhance efficiency, decision-making, and innovation, aligning AI systems with human teams remains a major challenge to realizing their full potential. Although academic interest is growing, the conceptual landscape of human–AI relationships remains fragmented. This study employs a bibliometric co-word analysis of 4093 peer-reviewed documents indexed in Scopus to map the intellectual structure of the field. Using a strategic diagram, we assess the relevance and maturity of five major thematic clusters identified in the field. Results highlight the structural dominance of Human–AI Interactions (Centrality: 1595), Human–AI Collaboration (1150), and Teaming and Augmentation (1131) as foundational themes, while Conversational AI (655), and Ethics and Responsibility (431) emerge as specialized domains. Based on the analysis, we propose a conceptual framework that classifies human–AI relationships into four categories—symbiotic, augmented, assisted, and substituted intelligence—according to the level of AI autonomy and human involvement. Rather than providing prescriptive guidance for practitioners, this framework is intended primarily as a scholarly contribution that clarifies the conceptual landscape and supports future theoretical and empirical work. While potential implications for organizational contexts can be inferred, these are secondary to the study’s main goal of offering a research-based synthesis of the field. Ultimately, our work contributes to academic consolidation by offering conceptual clarity and highlighting opportunities for future research, while underscoring the critical need for ethical alignment and interdisciplinary dialogue to guide future AI adoption. Full article

Neural network quantization has become established as a key strategy for transitioning medical imaging models from research environments to clinical devices and resource-constrained edge platforms; however, the available evidence remains fragmented and focused on highly heterogeneous use cases. This study presents a systematic review of 72 studies on quantization applied to medical images, following PRISMA guidelines, with the aim of characterizing the relationship among quantization technique, network architecture, imaging modality, and execution environment, as well as their impact on latency, memory footprint, and clinical deployment. Based on a structured variable matrix, we analyze—through tailored visualizations—usage patterns of Post-Training Quantization (PTQ), Quantization-Aware Training (QAT), mixed precision, and binary/low-bit schemes across frameworks such as PyTorch V 2.6.0, TensorFlow 2.19.0, and TensorFlow Lite, executed on server-class GPUs, edge/embedded devices, and specialized hardware. The results reveal a strong concentration of evidence in PyTorch/TensorFlow pipelines using INT8 or mixed precision on GPUs and edge platforms, contrasted with limited attention to PACS/RIS interoperability, model lifecycle management, energy consumption, cost, and regulatory traceability. We conclude that, although quantization can approximate real-time performance and reduce memory footprint, its clinical adoption remains constrained by integration challenges, model governance requirements, and the maturity of the hardware–software ecosystem. Full article

Drone-view mixed reality (MR) in the Architecture, Engineering, and Construction (AEC) sector faces significant self-localization challenges in low-texture environments, such as bare concrete sites. This study proposes an adaptive sensor fusion framework integrating thermal and visible light (RGB) imagery to enhance tracking robustness for diverse site applications. We introduce the Effective Inlier Count ($N_{eff}$) as a lightweight gating mechanism to evaluate the spatial quality of feature points and dynamically weigh sensor modalities in real-time. By employing a $20\times 16$ grid-based spatial filtering algorithm, the system effectively suppresses the influence of geometric burstiness without significant computational overhead on server-side processing. Validation experiments across various real-world scenarios demonstrate that the proposed method maintains high geometric registration accuracy where traditional RGB-only methods fail. In texture-less and specular conditions, the system consistently maintained an average Intersection over Union (IoU) above 0.72, while the baseline suffered from complete tracking loss or significant drift. These results confirm that thermal-RGB integration ensures operational availability and improves long-term stability by mitigating modality-specific noise. This approach offers a reliable solution for various drone-based AEC tasks, particularly in GPS-denied or adverse environments. Full article

Space-based gravitational wave detection relies on laser interferometry to measure picometer-level displacements over ${10}^5$–${10}^6$ km baselines. To integrate ranging and communication within the same optical link without degrading the primary scientific measurement, a low modulation index of 0.1 rad is required, resulting in extremely weak signals and challenging acquisition conditions. This study developed mathematical models for signal acquisition, identifying and analyzing key performance-limiting factors for both Binary Phase Shift Keying (BPSK) and Binary Offset Carrier (BOC) schemes. These factors include spreading factor, acquisition step, modulation index, and carrier-to-noise ratio (CNR). Particularly, the acquisition threshold can be directly calculated from these parameters and applied to the acquisition process of communication and ranging signals. Numerical simulations and evaluations, conducted with TianQin mission parameters, demonstrate that, for a data rate of 62.5 kbps and modulation indices of 0.081 rad (BPSK) or 0.036 rad (BOC), respectively, acquisition (probability ≈ 1) is achieved when the CNR is ≥104 dB·Hz under a false alarm rate of ${10}^{-6}$. These results provide critical theoretical support and practical guidance for optimizing the inter-satellite communication and ranging system design for the space-based gravitational wave detection missions. Full article

This paper presents Aerokinesis, an IoT-based software–hardware system for intuitive gesture-driven control of quadcopter unmanned aerial vehicles (UAVs), developed within the Robot Operating System 2 (ROS2) framework. The proposed system addresses the challenge of providing an accessible human–drone interaction interface for operators in scenarios where traditional remote controllers are impractical or unavailable. The architecture comprises two hierarchical control levels: (1) high-level discrete command control utilizing a fully connected neural network classifier for static gesture recognition, and (2) low-level continuous flight control based on three-dimensional hand keypoint analysis from a depth camera. The gesture classification module achieves an accuracy exceeding 99% using a multi-layer perceptron trained on MediaPipe-extracted hand landmarks. For continuous control, we propose a novel approach that computes Euler angles (roll, pitch, yaw) and throttle from 3D hand pose estimation, enabling intuitive four-degree-of-freedom quadcopter manipulation. A hybrid signal filtering pipeline ensures robust control signal generation while maintaining real-time responsiveness. Comparative user studies demonstrate that gesture-based control reduces task completion time by 52.6% for beginners compared to conventional remote controllers. The results confirm the viability of vision-based gesture interfaces for IoT-enabled UAV applications. Full article

This study analyzes the performance of artificial intelligence in processes known as “cognitive” (according to scientific literature) in comparison with the performance of human cognitive processes, analyzing experimental and/or empirical studies. The PRISMA process and bibliometric analysis were used to identify and analyze relevant research. A total of 291 studies were analyzed, which were grouped into five categories corresponding to the identified cognitive processes. The results show that only 10.3% of the studies report accuracy rates between 90% and 100% in their performance. The evidence suggests that AI can perform comparably to humans, but not with absolute efficiency. The experimental studies focus mainly on the “decision-making” process (56%), followed, in order of importance, by the processes of “analysis and evaluation” (25%), “judgment and reasoning” (8.6%), “comprehension and learning” (5.5%), and other “specific processes” (4.8%). The most significant contribution of this study is the comparative relational structure between human cognitive processes versus AI processes. Full article

This study re-evaluates ROC-AUC for binary classification under severe class imbalance (<3% positives). Despite its widespread use, ROC-AUC can mask operationally salient differences among classifiers when the costs of false positives and false negatives are asymmetric. Using three benchmarks, credit-card fraud detection (0.17%), yeast protein localization (1.35%), and ozone level detection (2.9%), we compare ROC-AUC with Matthews Correlation Coefficient, F2-score, H-measure, and PR-AUC. Our empirical analyses span 20 classifier–sampler configurations per dataset, combined with four classifiers (Logistic Regression, Random Forest, XGBoost, and CatBoost) and four oversampling methods plus a no-resampling baseline (no resampling, SMOTE, Borderline-SMOTE, SVM-SMOTE, ADASYN). ROC-AUC exhibits pronounced ceiling effects, yielding high scores even for underperforming models. In contrast, MCC and F2 align more closely with deployment-relevant costs and achieve the highest Kendall’s τ rank concordance across datasets; PR-AUC provides threshold-independent ranking, and H-measure integrates cost sensitivity. We quantify uncertainty and differences using stratified bootstrap confidence intervals, DeLong’s test for ROC-AUC, and Friedman–Nemenyi critical-difference diagrams, which collectively underscore the limited discriminative value of ROC-AUC in rare-event settings. The findings recommend a shift to a multi-metric evaluation framework: ROC-AUC should not be used as the primary metric in ultra-imbalanced settings; instead, MCC and F₂ are recommended as primary indicators, supplemented by PR-AUC and H-measure where ranking granularity and principled cost integration are required. This evidence encourages researchers and practitioners to move beyond sole reliance on ROC-AUC when evaluating classifiers in highly imbalanced data. Full article

This study investigates radio frequency (RF) wave propagation in sandstone and limestone cave environments, emphasizing the use of LiDAR-derived three-dimensional (3D) models to characterize cave geometry and support waveguide-based propagation analysis incorporating skin depth effects. RF transmission and reception measurements were conducted under line-of-sight (LOS) conditions across frequency bands from Low Frequency (LF) to Ultra-High Frequency (UHF). Comparative results reveal distinct attenuation behaviors governed by differences in cave geometry and electrical properties. The sandstone cave, with a more uniform geometry and relatively higher electrical conductivity, exhibits lower attenuation across most frequency bands, whereas the limestone cave shows higher attenuation due to its irregular structure. LiDAR-based 3D models are employed to extract key geometric parameters, including cavity dimensions, wall roughness, and wall inclination, which are incorporated into the proposed analytical framework. The model is further validated using experimental field measurements, demonstrating that the inclusion of LiDAR-derived geometry and skin depth effects enables a more realistic representation of underground RF propagation for communication system analysis. Full article

Individual animal identification is a cornerstone of animal welfare practices and is of crucial importance for food safety and the protection of humans from zoonotic diseases. It is also a key prerequisite for enabling automated processes in modern dairy farming. With newly emerging technologies, visual animal identification based on machine learning offers a more efficient and non-invasive method with high automation potential, accuracy, and practical applicability. However, a common challenge is the limited variability of training datasets, as images are typically captured in controlled environments with uniform backgrounds and fixed poses. This study investigates the impact of foreground segmentation and background removal on the performance of convolutional neural networks (CNNs) for cow identification. A dataset was created in which training images of dairy cows exhibited low variability in pose and background for each individual, whereas the test dataset introduced significant variation in both pose and environment. Both a fine-tuned CNN backbone and a model trained from scratch were evaluated using images with and without background information. The results demonstrate that although training on segmented foregrounds extracts intrinsic biometric features, background cues carry more information for individual recognition. Full article