In battlefield scenarios or hostile environments, rapid access to specialized medical assistance can be the difference between life and death. SMARTMED proposes an advanced military telemedicine solution that combines real-time biometric data with augmented reality (AR) to guide non-specialist personnel in emergency procedures and ensure continuous monitoring of vital signs in wounded individuals, even before a physician arrives on site. Key objectives include continuous monitoring of critical parameters (ECG, SpO₂, blood pressure, respiratory rate) via wearable modules integrated into combat vests, step by-step AR assistance using transparent display helmets, secure connectivity to remote medical command centers, and adaptive feedback with automatic protocol updates. The system architecture encompasses mobile acquisition nodes, portable field stations, medical command centers, and adaptive machine learning mechanisms. Innovations include contextual AR interfaces, distributed edge computing, multi-stage alerting, and lightweight blockchain for data integrity. Implementation plans involve hardware prototyping, simulations with volunteers, field testing in military polygons, and ML module deployment. Expected impacts include a 30% reduction in mortality within the "golden hour," over 50% decrease in medical errors by non-specialists, and enhanced coordination between front lines and medical back offices. Keywords: telemedicine, augmented reality, vital signs monitoring, military medicine, machine learning, blockchain, edge computing.
This paper analyzes the use of adaptive fuzzy logic as an intelligent optimization method in Integrated Mechatronic Systems for Physical Security (SMSF). This approach combines the numerical precision of automatic control with the decision-making flexibility specific to linguistic reasoning, enabling efficient management of uncertainty and variability within the operational environment. By integrating machine learning mechanisms such as the ANFIS model, these systems gain self-adjusting and continuously optimizing capabilities for control parameters, ensuring high robustness, stability, and decision-making autonomy. The application of adaptive fuzzy logic within the SMSF architecture contributes to reducing false alarms, increasing resilience and adaptability, and fostering the development of cognitive mechatronics. The study highlights the potential of this technology to transform security systems from reactive structures into proactive entities capable of learning and responding intelligently to complex threats. Keywords: adaptive fuzzy logic, mechatronics, intelligent optimization, ANFIS, physical security, adaptive control.
We develop a fractional-weighted functional-analytic framework for the analysis of chaotic dynamics in which the governing equations remain classical while the geometry of the underlying Hilbert space is modified. Specifically, we introduce a family of fractional scalar products with singular weights derived from the Riemann–Liouville kernel, generating weighted Hilbert spaces that emphasize late-time dynamics and long-term correlations. Within this framework, the fractional parameter α plays a dual role by controlling temporal localization in the scalar product and acting as an effective probe of dynamical complexity. By embedding trajectories of the classical Lorenz system into these spaces, we show that the value α _min minimizing the normalized fractional norm exhibits a clear nonlinear correlation with the Kaplan–Yorke dimension D_KY of the attractor, thereby establishing α _min as a functional proxy for fractal complexity without modifying the underlying dynamics. To support analysis and computation, we construct orthogonal and complete basis systems adapted to the fractional geometry, including weighted Gram–Schmidt bases and Jacobi polynomial expansions, which enable efficient spectral approximation of chaotic signals and reveal intrinsic temporal asymmetries not captured by standard L^2 representations. The proposed approach provides new analytical and spectral tools for detecting bifurcations, quantifying chaotic complexity, and representing fractal structure, offering a complementary alternative to existing methods based on fractional-order dynamical models.
The research focuses on the development and evaluation of a radio signal processing and analysis module, utilizing RTL-SDR equipment in combination with a Raspberry Pi 4 Model B embedded computer. By applying signal processing algorithms such as the Fast Fourier Transform (FFT), FIR/IIR filters, and CFAR detection techniques, the software was tested using signal data captured across various frequency bands. The results demonstrate that the system is capable of detecting real signals with high speed and accuracy. Based on these findings, the study proposes solutions to enhance the application, including improving sensitivity and processing speed, integrating artificial intelligence to analyze complex signals, implementing remote control capabilities, and expanding the system's practical applications in both civilian frequency monitoring and military operations.
Specialized mobile robots for under-bridge inspection and maintenance play a crucial role in ensuring the safety and operational reliability of transportation infrastructure. This paper focuses on kinematic analysis and the optimization of operational positioning to enhance equipment efficiency. First, a kinematic model is developed based on the Denavit–Hartenberg (D-H) matrix method, enabling the accurate determination of the workspace envelope and the effective working radius. Based on these kinematic characteristics and optimization theory, the study proposes an algorithm for optimizing the base vehicle's stopping positions, subject to strict geometric constraints regarding surface coverage and safe overlap margins. Simulation results demonstrate that the proposed approach identifies an optimal step size that guarantees 100% accessibility to the bridge underside while minimizing the number of vehicle stops, thereby significantly improving operational efficiency.
