Search Results (156)

Electronic attack (EA) using jamming signals is an essential component of electronic warfare (EW), consisting of the use of electromagnetic energy to disrupt, block or reduce the effectiveness of enemy communications, radar and navigation systems. In current conflicts, jamming EA is also successfully used against drones (air/ground), which have become an important component of modern warfare. In this context, the article proposes a method of protection against jamming (anti-jamming) for drone communications, thus achieving their resilience to electronic attack. The proposed method combines the wavelet transform with threshold SURE, adaptive filtering (LMS) and Bidirectional Long Short-Term Memory for anti-jamming resilience of 64-QAM (quadrature amplitude modulation) communication used by drones, evaluates the performance of the results obtained against electronic warfare systems and presents the open challenges for future research. Full article

This paper presents an interval-observer-based actuator fault detection and isolation (FDI) method for underactuated autonomous underwater vehicles (AUVs) under bounded hydrodynamic uncertainty and time-varying ocean currents. A locally frozen linear time-invariant (LTI) representation enables deterministic set-membership analysis, and the robust adaptive propagated interval observer (RAPIO) propagates admissible center–radius state bounds within a Lyapunov framework. Adaptivity is introduced through a reinforcement learning (RL)-augmented uncertainty-bound modulation mechanism, where an offline-trained agent scales a nonnegative channel-wise slack term without modifying the scheduled observer-gain rule or the nominal center predictor. Under the stated observer and disturbance-envelope conditions, positivity, stability, and diagnostic-channel inclusion hold for any bounded learning signal. Actuator loss-of-effectiveness (LoE) faults are represented through the actuator-effectiveness channel and detected through interval-consistency violations, enabling axis-wise isolation of surge, yaw-rate, and pitch-rate actuator faults. The same schedule-blind decision layer is additionally evaluated with structurally distinct additive-bias and stuck/jam actuator models. All stuck/jam events are detected, and bias-magnitude sweeps identify channel-wise 100%-detection boundaries with zero false alarms. A structured 72-case scenario sweep shows reliable detection, strong false-alarm rejection, and acceptable detection delays compared with benchmark observers. Full article

Cooperative jamming task allocation for UAV swarms must jointly consider radar priority, the effectiveness of different jamming modes, and heterogeneous resource limits. This paper establishes a radar threat assessment model from radar operating parameters and evaluates each candidate jamming assignment in the time, frequency, and power domains. Based on these evaluations, a binary integer programming model is formulated to jointly determine radar selection, UAV assignment, and jamming mode under coverage, capacity, and mode availability constraints. To solve the model, an improved multi-population genetic algorithm (IMPGA) is developed using crossover on radar task blocks, mutation at the task level, stochastic feasibility repair, and cooperative evolution among multiple subpopulations. Experimental comparisons are conducted under identical function evaluation budgets, and each representative scenario is evaluated through 100 independent Monte Carlo runs. The results show that the IMPGA reliably obtains exact or near-optimal solutions and provides improved solution quality and consistency, particularly as the problem scale and resource coupling increase. The scalability experiments further demonstrate that the algorithm maintains small optimality gaps in larger instances. Ablation results confirm that the radar task operators and stochastic repair make important contributions to the final solution quality and convergence process. Full article

The dynamics of active particles are increasingly being leveraged to design and control micro-robotic swarms. Local interactions play a crucial role in the phase transitions of active particles; how the combined effects of alignment, short-range repulsion, and boundary interactions regulate their packing structure and collective order with different confinement scales remains less systematically explored. In this study, we investigate the packing of active particles within a confined region, focusing on the role of local interaction rules in shaping both the packing structure and the polar order parameter. The effects of key controlling variables related to local interaction rules, including interaction radius, repulsion radius, confined boundary radius, and noise strength, are numerically studied. Specifically, by comparing systems with and without velocity–alignment interactions, we reveal the role of alignment in dictating both structural and dynamical properties of the ensemble. To quantify the packing structure, we employ Voronoi tessellation to evaluate both local and global packing densities. The results show that strong confinement induces a jammed state in which alignment effects are suppressed, resulting in high global packing density and low polar order, regardless of the noise amplitude. Upon increasing the boundary radius beyond a critical threshold, the system unjams, enabling alignment interactions to significantly enhance both the polar order parameter and packing density. Interestingly, the relationship between global packing density and micro-structural parameters, such as coordination number and Voronoi tessellation metrics, is similar in the systems with and without alignment. Our results demonstrate that collective packing and phase behaviour of active matter are governed by the nontrivial interplay between alignment, confinement, and noise, with alignment interactions driving the transition from disordered to ordered states as geometric constraints are relaxed, offering critical insights for the design of targeted micro-robotic swarms and active microfluidic sorting systems. Full article

Global navigation satellite systems (GNSSs) are essential for navigation in aviation, transportation, and autonomous systems, yet they remain vulnerable to intentional interference such as jamming and spoofing. Unlike prior studies that primarily focus on positioning error, this work emphasizes acquisition-phase behavior, analyzing the impact of interference on time-to-first-fix (TTFF) and post-attack reacquisition time. A controlled and repeatable laboratory testbed based on software-defined radio (SDR) was developed to emulate Global Positioning System (GPS) L1 and Galileo E1 signals under multiple interference scenarios, including narrowband jamming, static spoofing, and dynamic spoofing. Five commercial GNSS receivers were evaluated under identical conditions. The results show that jamming causes an immediate loss of positioning capability, reducing the empirical navigation-fix probability to near zero and significantly increasing reacquisition time, with recovery-phase empirical fix probabilities ranging from 0.062 to 0.991 depending on receiver class. In contrast, spoofing maintains high attack-phase empirical navigation-fix probabilities ranging from 0.730 to 0.907 while introducing persistent and undetected errors. Static position spoofing was found to produce position offsets that persisted into the recovery phase, delaying the return to the authentic navigation solution. For most receivers, however, correct positioning was restored within the observation window. Multi-constellation spoofing further increases attack effectiveness, raising fix continuity by more than 0.15 compared to single-constellation cases. Multi-band receivers demonstrate increased resilience by delaying spoof acceptance by more than 4 min in extended scenarios, rather than preventing it entirely. The proposed methodology enables reproducible evaluation of GNSS receiver robustness and demonstrates that navigation-fix continuity alone is not a reliable indicator of navigation integrity during spoofing attacks. Overall, the results demonstrate that navigation-fix continuity alone cannot be regarded as a reliable indicator of navigation integrity and highlight the importance of complementary integrity-monitoring mechanisms for GNSS-dependent systems. The reported observations were obtained under controlled laboratory conditions and should be interpreted within the context of the adopted experimental methodology rather than as a direct representation of operational performance in real-world environments. Full article

This paper proposes a novel jamming multiplexing communication method for synthetic aperture radar (SAR) imaging in scatter-wave jamming (SWJ) scenarios. This method multiplexes communication information by modulating a weak phase in the slow time dimension of the SWJ signal. After receiving the signal, the ground communication station (CS) extracts the information according to the designed algorithm, thus achieving communication transmission. Specifically, a detailed signal model is established to describe the modulation and demodulation mechanism of the communication information. The impact of communication modulation on the SAR image is theoretically analyzed, and the bit error rate (BER) performance of the communication transmission is evaluated. Due to the use of high-gain matched filtering technology in the communication demodulation process, the proposed method achieves better communication BER performance in low signal-to-noise ratio (SNR) environments without significantly reducing jamming performance. Simulation results show that the proposed method achieves reliable information transmission while maintaining effective SWJ capability. Full article

To address robust polarization-domain adaptive anti-jamming for dual-polarized radars with limited secondary data and time-varying interference, this paper proposes a covariance-reliability-driven MVDR framework based on forgetting-factor covariance estimation and adaptive diagonal loading. The forgetting-factor recursion assigns larger weights to recent jammer-plus-noise snapshots to track nonstationary interference, while the adaptive loading coefficient is jointly controlled by sample deficiency and covariance condition-number degradation to improve inversion stability. Unlike many robust adaptive beamforming methods that require steering-vector uncertainty sets, mismatch distributions, or subspace information, the proposed method relies only on secondary data and a small set of scalar design parameters. Simulation results based on a synthetic dual-polarized array model show that the proposed method achieves competitive output SINR, effective jammer suppression, and improved robustness to moderate DOA and polarization mismatch under limited-snapshot and time-varying interference conditions. Complexity analysis indicates that the proposed method has the same dominant computational order as standard covariance-based MVDR beamforming, apart from condition-number evaluation. The present validation is simulation-based, and further verification using measured polarimetric radar data, realistic propagation models, or hardware experiments is still required. Full article

In this paper, a reconfigurable multi-form radar compound coherent jamming signal generator is proposed based on a dual-polarization quadrature phase shift keying (DP-QPSK) modulator cascaded with an intensity modulator (IM). The radar signal and jamming seed signal are loaded on the upper path and the lower path of the DP-QPSK modulator to achieve carrier-suppressed single-sideband (CS-SSB) modulation and phase modulation, respectively. The periodic rectangular pulse (PRP) signal is fed into the IM to achieve interrupted-sampling repeater jamming in the optical domain. In our proposed scheme, cosine phase modulation and interrupted-sampling repeater jamming (CPMJ-ISRJ) and frequency shift and interrupted-sampling repeater jamming (FSJ-ISRJ) are obtained only by changing the form of the jamming seed signal, without changing the overall structure of the scheme. The jamming effectiveness of the above schemes is evaluated through simulation. Multiple false targets are obtained after cross-correlation with the original radar signal. The number of generated false targets can reach 18. We also conducted a detailed simulation to analyze the impact of different parameters on the jamming effect. Because the scheme is filter-free, it has a large frequency tuning range. Moreover, due to the special CS-SSB modulation, the modulated signals are immune to the chromatic dispersion-induced power fading effect. The proposed scheme has potential application prospects in future electronic countermeasure systems. Full article

Background/Objectives: Contemporary minimally invasive surgical therapies (MISTs) for benign prostatic hyperplasia carry retreatment rates up to 32%, with holmium laser enucleation of the prostate (HoLEP) increasingly used as salvage therapy. Prior reviews focused on salvage HoLEP (sHoLEP) following transurethral resection; however, technical challenges specific to the post-MIST field remain uncharacterised. We aimed to characterise technical barriers during sHoLEP following contemporary MISTs, with secondary evaluation of efficacy, safety and feasibility. Methods: Following preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines (PROSPERO: CRD420261321711), five databases were searched from inception to February 2026. Studies reporting sHoLEP outcomes in adults with prior MIST were included. Qualitative findings were synthesised thematically; quantitative outcomes reported by three or more studies underwent random-effects meta-analysis. Risk of bias was assessed using methodological index for non-randomized studies methodological index for non-randomized studies (MINORS) and certainty of evidence using grading of recommendations, assessment, development, and evaluation (GRADE). Results: Ten studies (354 sHoLEP, 3618 primary HoLEP (pHoLEP) patients) were included. Technical difficulty was MIST-type dependent: thermoablative procedures and prostatic artery embolisation preserved the enucleation plane, while prostatic urethral lift (PUL) introduced morcellation-specific challenges including blade jamming and staged procedures. Meta-analysis revealed no difference in operative time or tissue weight, but reduced enucleation efficiency (weighted mean difference; WMD −0.11 g/min, p = 0.027) and peak urinary flow improvement (WMD −3.0 mL/s, p < 0.001). Both findings were sensitive to analysis, losing significance on restriction to predominantly MIST cohorts, and the enucleation efficiency result additionally lost significance on removal of the most heavily weighted study (p = 0.94). Complication rates were equivalent (odds ratio (OR) 0.92, p = 0.787). Conclusions: sHoLEP is safe and efficacious following contemporary MIST. Surgeons should anticipate MIST-specific challenges, particularly morcellation difficulties after PUL requiring tailored instrumentation. Prospective MIST-specific studies are needed. Full article

Inspired by the human hand, this work presents a hybrid rigid–soft gripper that achieves passive adaptability through a self-resetting granular jamming pouch integrated onto a 3-DOF cable-driven rigid skeleton. Seven fingertip configurations (rigid tip, different jamming particles, and TPU-only) were evaluated across five object geometries. The jamming pouch configurations showed a clear advantage over rigid fingertips and a modest improvement over TPU-only fingertips when grasping flat or smoothly curved surfaces, while demonstrating substantially superior performance for objects with sharp protrusions, lips, undercuts, or deformable edges, where enhanced conformability and geometric interlocking markedly improved payload capacity and lateral offset tolerance. The passive self-reset mechanism remained reliable over 1000 cycles. These results demonstrate that the hybrid design effectively combines the advantages of rigid and soft grippers, achieving superior overall grasping performance while balancing adaptability and payload without pneumatic actuation, with strong potential for applications in logistics, food handling, and mobile robotics. Full article

Sustainable production in dental and orthodontic 3D printing has gained increasing attention due to environmental concerns and the need for cost-effective and resource-saving solutions. This study presents a proof of concept for using recycled polymers and fused granular fabrication (FGF) in a closed-loop 3D printing approach, omitting intermediate filament manufacturing. A desktop 3D printer served as the kinematic platform and was modified with a pellet-based extruder to directly process recycled polyethylene terephthalate glycol (PETG) flakes, obtained by shredding previously printed PETG parts, into dental models. Dimensional accuracy was evaluated using optical 3D scanning analysis. The results indicate that models produced from recycled PETG are, in principle, suitable for dental and orthodontic applications within the investigated scope. This technical note provides initial evidence supporting the integration of recycled thermoplastics into dental and orthodontic model fabrication as part of sustainable additive manufacturing workflows. Potential pathways for workflow integration in clinical and laboratory environments, as well as directions for future research, are outlined, including the optimization of printing parameters and process stability. The main technical challenges were unreliable feedstock flow, causing bridging and jamming, while thermal creep from insufficient inlet cooling promoted premature softening of the flakes, causing torque spikes and unstable feeding. Full article

Timely situational awareness is essential in disaster management but normal Unmanned Aerial Vehicle (UAV) flight cannot take place when the Global Positioning System (GPS) signals are blocked or jammed. This paper addresses the issue of swarm cohesion and localization in these hostile conditions. We present a Cooperative Swarm-Mesh Network (CSMN), a hybrid structure that can alternate between an implicit Silent Mode and an explicit Leader–Follower mode based on distributed Extended Kalman Filters (DEKFs) in the face of communication failures. The system takes advantage of convex polygon decomposition to optimize the coverage in the area. The use of simulation studies with NS-3 and ROS has shown that the proposed framework can retain sub-meter localization error (RMSE < 0.9 m) in GPS-denied environments and provide 92% coverage of the area, which is 35% higher than the coverage with other baseline approaches. Within the simulated conditions evaluated using Gazebo/NS-3, sensor drift and network vulnerability are effectively addressed by the CSMN framework. These simulation-based results offer a promising blueprint for autonomous disaster evaluation, pending hardware-in-the-loop and field validation. Validation is conducted across two qualitatively distinct simulated environments: dense urban rubble and a sparse open field. Performance advantages generalise beyond a single test configuration, with mean localization RMSE remaining below 0.85 m in both scenarios. Full article

The valorization of agro-industrial waste streams represents a promising strategy for reducing production costs in microalgae biotechnology while promoting circular economy approaches. In this study, wastewater derived from fig jam processing was evaluated as an organic carbon source for mixotrophic cultivation of Limnospira (Spirulina) platensis. Cultures were grown under four conditions: a control medium and three concentrations of fig wastewater (FW) at 0.75%, 1.5%, and 3% (v v⁻¹). The wastewater used in this study originates specifically from the washing and cleaning stages of dried fig processing, representing an early processing stream characterized by relatively high soluble sugar content and low thermal or chemical alteration. Biomass biochemical composition and bioactive compound production were investigated, including carbohydrates, proteins, lipids, photosynthetic pigments, polyphenols, antioxidant activity, and phycocyanin extraction yield and purity. The results showed that fig wastewater supplementation significantly influenced the metabolic profile of L. platensis. The highest protein content was obtained at 0.75% FW (44.90 ± 1.93 g 100 g⁻¹ DW), whereas lipid accumulation increased with FW concentration, reaching 9.45 ± 2.30 g 100 g⁻¹ DW at 3% FW. Antioxidant activity peaked at 1.5% FW (4.33 ± 0.43 μmol Trolox mg⁻¹ DW), suggesting stimulation of oxidative stress response pathways under moderate organic supplementation. Pigment production showed different responses, with relatively stable chlorophyll and carotenoid contents but decreasing phycocyanin levels at higher FW concentrations. Phycocyanin yield decreased from 9.82 ± 1.00 g 100 g⁻¹ DW in the control to 5.80 ± 0.22 g 100 g⁻¹ DW at 3% FW, while purity values were highest at the highest FW concentration. These findings demonstrate that fig processing wastewater can be effectively used as an alternative organic substrate for mixotrophic Spirulina cultivation, enabling simultaneous wastewater valorization and production of biomass rich in proteins and bioactive compounds. Full article

Unmanned aerial vehicles (UAVs) are increasingly deployed in missions that require high autonomy and reliable decision-making; however, many operational concepts still assume access to GNSS and stable communication with a human operator. In contested environments, this assumption may no longer hold because GNSS degradation, radio-frequency interference, and intentional jamming can disrupt positioning and communication, thereby reducing mission effectiveness and safety. Recent surveys show that operation in GNSS-denied environments remains a major challenge and often requires alternative perception, localization, and control strategies. In response, this article investigates a reinforcement learning (RL)-based decision-support system for the autonomous control of a quadrotor UAV in a three-dimensional simulated environment. Rather than following pre-programmed waypoints, the UAV learns a control policy through interaction with the environment and reward-driven adaptation. The proposed system is designed for mission execution under uncertainty, limited external guidance, and partial observability. Two policy-gradient approaches are implemented and compared: classical REINFORCE and Proximal Policy Optimization (PPO) with an Actor–Critic architecture. The study presents the simulation environment, state and action representation, reward formulation, staged training procedure, and comparative evaluation. The results indicate that, within the considered unseen test scenario, the PPO-based configuration achieved higher mission effectiveness than REINFORCE in the final unseen test scenario, supporting the practical relevance of structured deep reinforcement learning for UAV operation in GPS-denied and communication-constrained environments. Full article

The increasing interest in the development and integration of navigation and positioning services across a wide range of receivers has exposed them to various security threats, including GNSS jamming and spoofing attacks. Early detection of jamming and spoofing interference is crucial to mitigating these threats and preventing service degradation. This research introduces an interference detection technique leveraging an AI algorithm applied to GNSS data utilizing various methods to enhance detection accuracy and efficiency. The objective was to use modern sensors and AI to develop an effective tool that detects, characterizes, and localizes interference, thereby reducing associated risks. These sensors and algorithms enable continuous GNSS interference monitoring and support real-time Decision-making. A server plays a crucial role in managing the entire system. Its primary function is to process data collected from various sensors referred to as nodes (e.g., static, rover, drone, and space) and from (public) GNSS networks as well as to perform localization using rotating-antenna nodes. Within the interference detection module, various methods were implemented at different points in the software receiver architecture. Each method’s certainty in identifying an interference source depends on its design and capabilities, with outcomes—whether positive or negative—being subject to potential accuracy or errors. To enhance the Decision-making process, an AI-based Decision-making block has been introduced to determine the presence of interference at a given epoch. The proposed interference monitoring methods were evaluated through experiments using GNSS signals under clean, jamming, and spoofing scenarios. The results demonstrate the techniques’ applicability across diverse scenarios, achieving high performance in interference detection, characterization, and localization. Full article