Search Results (79)

For vehicle driving scenarios in complex backgrounds, road sign detection faces challenges such as multi-scale targets, long-distances, and low-resolution. To address these challenges, a detection method based on an improved YOLO11n network is proposed. Firstly, to accommodate the multi-scale characteristics of the targets and improve the network’s ability to detect low-resolution objects and details, a Multi-path Gated Aggregation (MGA) Module is proposed, achieving these objectives via multi-dimensional feature extraction. Secondly, the Neck is improved by designing a network structure that incorporates high-resolution information from the Backbone, thereby enhancing the detection capabilities for small and blurry targets. Finally, an enhanced Spatial Pyramid Pooling—Fast (SPPF) module is proposed, wherein a Group Convolution-Layer Normalization-SiLU structure is integrated across various stages of information passing. By fusing adjacent channel information, it effectively suppresses complex background noise across multiple scales and amplifies road marking features, which consequently boosts the model’s discriminability for distant and obscured targets. Experimental results on a multi-type road sign dataset show that the improved model achieves an mAP@0.5 of 96.96%, which is 1.42% higher than the original model. The mAP@0.5–0.95 and Recall rates are 83.94% and 92.94%, respectively, while the inference speed remains at 134 FPS. Research demonstrates that via targeted modular designs, the proposed approach strikes a superior balance between detection accuracy and real-time efficiency. Consequently, it provides robust technical support for the reliable operation of intelligent vehicle perception systems under complex conditions. Full article

Background/Objectives: Individuals with diabetes often experience difficulties in the healing of their alveolar sockets. Furthermore, obesity is strongly associated with the development and progression of type 2 diabetes through complex metabolic and inflammatory mechanisms. The current study provides new insights into the use of Luteolin (LU) and/or chitosan vesicles (CHV) to accelerate bone regeneration, highlighting a biologically and clinically relevant approach that leverages implants as a clinical solution. Methods: Sixty rats were randomly categorized into five groups: Group I (negative control); Group II (positive control), diabetic and obese rats; Group III (LU-treated), diabetic and obese rats with an extraction socket loaded with LU; Group IV (CHV-treated), diabetic and obese rats with an extraction socket loaded with CHV; and Group V (LU-CHV), diabetic and obese rats with an extraction socket loaded with LU-CHV. After 2 and 6 weeks, rats’ mandibles underwent histological, histomorphometric, biochemical, and statistical analyses. Results: The results demonstrated significant differences among the experimental groups. The LU-CHV formulation showed superior therapeutic performance compared with free luteolin and the untreated control group. In vitro release studies revealed sustained, controlled release from LU-CHV, whereas free luteolin exhibited rapid drug release. Additionally, LU-CHV significantly enhanced biological activity, as evidenced by improved anti-inflammatory and/or therapeutic markers compared to the other groups. These findings indicate that encapsulation within chitosan vesicles improved drug stability, bioavailability, and overall therapeutic efficiency. Conclusions: LU-CHV demonstrated superior efficacy compared to free luteolin, highlighting the advantage of chitosan-based vesicular delivery systems. LU-CHV not only enhanced controlled drug release and therapeutic outcomes but also presents a promising platform that could significantly advance targeted drug delivery strategies in inflammatory and metabolic disorders. The findings suggest that LU-CHV represents a transformative approach in improving treatment effectiveness and patient outcomes. Full article

The Dengshang Mo deposit is a recently recognized porphyry-type system within the Yanliao Mo metallogenic belt of northern Hebei Province. However, its ore-hosting rhyolitic porphyry emplacement age and petrogenesis remain insufficiently understood. This study integrates petrography, zircon U–Pb geochronology, Lu–Hf isotope analysis, zircon trace element geochemistry, and whole-rock major- and trace element data to investigate the petrogenesis of the Dengshang rhyolitic porphyry and its genetic relationship with Mo mineralization. The ore-hosting porphyry is predominantly composed of quartz and plagioclase phenocrysts. LA-ICP-MS zircon U–Pb dating yields an emplacement age of 168.3 ± 1.2 Ma, indicating that the rhyolitic porphyry was emplaced during a Middle Jurassic magmatic episode. Petrological and geochemical characteristics classify the Dengshang rhyolitic porphyry as an I-type granite. Zircon ε_Hf(t) values range from −0.93 to −7.29, corresponding to two-stage model ages (T_DM²) of 1.27–1.67 Ga, which suggests derivation from partial melting of the Mesoproterozoic lower crust. Zircon trace elements display significant positive Ce anomalies (δCe = 10.14–332.85), and calculated oxygen fugacities (ΔFMQ = −0.65 to +2.77; median = +0.51) indicate moderately oxidized magmatic conditions conducive to Mo enrichment. These results collectively imply that the Dengshang rhyolitic porphyry was emplaced at ~168 Ma associated with paleo-Pacific plate subduction. This geodynamic setting triggered partial melting of Mesoproterozoic lower crust, producing oxidized magmas that experienced fractional crystallization prior to shallow emplacement. Our findings elucidate the petrogenesis of the Dengshang rhyolitic porphyry and its control on Mo mineralization, and contribute new insight for understanding porphyry Mo genesis within the complex tectonic evolution of the Yanliao Mo Belt. Full article

This paper presents a fault-tolerant control (FTC) strategy for a six-degree-of-freedom (DoF) anthropomorphic manipulator operating under actuator faults and complex friction dynamics. The proposed framework integrates a backstepping control methodology with LuGre friction modeling and a feedforward neural network (FFNN) for friction estimation. Actuator faults are considered in the form of multiplicative efficiency losses and additive disturbances. An adaptive control law is developed to estimate and compensate for both friction and actuator faults in real time. The stability of the closed-loop system is guaranteed through Lyapunov theory. The simulation results validate the effectiveness and robustness of the proposed approach in ensuring precise trajectory tracking despite faults and friction uncertainties. Full article

In this study, an analysis of fractional-order Lü systems is performed through a framework approach consisting of analytical solution strategies in combination with numerical methods. On the analytical methodology front, the recently developed form of the new generalized differential transform method (NGDTM) is adopted for its efficiency in providing an approximate solution with high capability in tracking the behavior of these systems. On the other hand, the Grünwald–Letnikov via Riemann–Liouville scheme (GLNS) is adopted within this study as one of its tools in confirming whether chaos exists within these systems. The performance and accuracy of the proposed method are also rigorously tested, and comparisons are made numerically with the Adams–Bashforth–Moulton method, which is used here as a standard method for validation purposes. It is clear from the results that the combination of analytical and numerical methods can greatly enhance both the speed of computation and the accuracy of results. Additionally, the proposed method or approach is found to be quite robust and accurate and can thus be employed for analyzing various fractional dynamical systems that display chaotic attractors. The proposed method can also be expanded upon in the future for analyzing complex models in science and engineering. Full article

The growing threat of unauthorized drones to ground-based critical infrastructure necessitates efficient ground-to-air surveillance systems. This paper proposes a lightweight framework for UAV detection and altitude measurement from a fixed ground perspective. We introduce Ghost-YOLOv11n, an optimized detector that integrates GhostConv modules into YOLOv11n, reducing computational complexity by 12.7% while achieving 98.8% mAP_0.5 on a comprehensive dataset of 8795 images. Deployed on a LuBanCat4 edge device with Rockchip RK3588S NPU acceleration, the model achieves 20 FPS. For stable altitude estimation, we employ an Extended Kalman Filter to refine measurements from a monocular ranging method based on similar-triangle geometry. Experimental results under ground monitoring scenarios show height measurement errors remain within 10% up to 30 m. This work provides a cost-effective, edge-deployable solution specifically for ground-based anti-drone applications. Full article

Fractional-order chaotic systems describe complex dynamic processes with memory effects and long-range correlations, while integer-order chaotic systems are generally viewed as a special case of fractional-order counterparts. This close relationship often renders the two difficult to distinguish in practice. However, existing studies mostly design analytical methods for integer-order or fractional-order chaotic systems separately, lacking a unified classification framework that does not rely on prior assumptions about the system order. In this paper, we propose a multi-branch deep learning model integrating a multi-scale convolutional neural network, time–frequency analysis, Transformer blocks, and dynamic memory network to classify integer-order chaos, fractional-order chaos, and steady states. Experiments are conducted on time series from canonical chaotic systems—including the Lorenz, Rössler, Lü, and Chen systems—in both integer- and fractional-order formulations, under two data generation protocols: varying initial conditions and varying system parameters. We evaluate the model under two scenarios: (1) assessing baseline classification performance on noise-free data and (2) testing robustness against increasing levels of Gaussian, salt-and-pepper and Rayleigh noise. The model achieves classification accuracy above 99% on noise-free data across all tested systems. Under noise interference, it demonstrates strong robustness: accuracy remains above 89.7% under high-intensity Gaussian noise. Moreover, we demonstrate that the model trained with fixed system parameters but varying initial conditions generalizes poorly to unseen parameter settings, whereas training with diverse system parameters—while fixing initial conditions—markedly improves generalization. This work offers a data-driven framework for distinguishing integer- and fractional-order chaos and highlights the critical role of training data diversity in building generalizable classifiers for dynamical systems. Full article

This paper presents fine-grained Field Programmable Gate Arrays (FPGA) architectures for the Advanced Encryption Standard (AES) MixColumns and InvMixColumns transformations, targeting improved performance and resource utilization. The proposed method reformulates these operations as boolean functions directly mapped onto FPGA Lookup-Table (LuT) primitives, replacing conventional xor-based arithmetic with memory-level computation. A custom MATLAB-R2019a-based pre-synthesis optimization algorithm performs algebraic simplification and shared subexpression extraction at the polynomial level of Galois Field $GF(2^8)$, reducing redundant logic memory. This architecture, LuT-level optimization minimizes the delay of the complex InvMixColumns stage and narrows the delay gap between encryption (1.305 ns) and decryption (1.854 ns), resulting in a more balanced and power-efficient AES pipeline. Hardware implementation on a Xilinx Virtex-5 FPGA confirms the efficiency of the design, demonstrating competitive performance compared to state-of-the-art FPGA realizations. Its fast performance and minimal hardware requirements make it well suited for real-time secure communication systems and embedded platforms with limited resources that need reliable bidirectional data processing. Full article

As embedded and IoT systems demand secure and compact encryption, developing cryptographic solutions that are both lightweight and efficient remains a major challenge. Many existing AES implementations either lack flexibility or consume excessive hardware resources. This paper presents an area-efficient and flexible AES-128 implementation based on a hardware/software (HW/SW) co-design, specifically optimized for platforms with limited hardware resources, resulting in reduced power consumption. In this approach, key expansion is performed in software on a lightweight MicroBlaze processor, while encryption and decryption are accelerated by dedicated hardware IP cores optimized at the Look-up Table (LuT) level. The design is implemented on a Xilinx XC5VLX50T Virtex-5 FPGA, synthesized using Xilinx ISE 14.7, and tested at a 100 MHz system clock. It achieves a throughput of 13.3 Gbps and an area efficiency of 5.44 Gbps per slice, requiring only 2303 logic slices and 7 BRAMs on a Xilinx FPGA. It is particularly well-suited for resource-constrained applications such as IoT nodes, secure mobile devices, and smart cards. Since key expansion is executed only once per session, the runtime is dominated by AES core operations, enabling efficient processing of large data volumes. Although the present implementation targets AES-128, the HW/SW partitioning allows straightforward extension to AES-192 and AES-256 by modifying only the software Key expansion module, ensuring practical scalability with no hardware changes. Moreover, the architecture offers a balanced trade-off between performance, flexibility and resource utilization without relying on complex pipelining. Experimental results demonstrate the effectiveness and flexibility of the proposed lightweight design. Full article

The Eastern Sag of the Liaohe Depression, situated in the Bohai Bay Basin, represents a key area for hydrocarbon exploration in northeastern China. Despite decades of research, the mechanisms governing its complex structural evolution remain unclear, largely due to multiple tectonic reactivations associated with the Tan–Lu Fault Zone. In this study, newly acquired deep seismic reflection data were used to interpret representative structural profiles across the sag. Complementary sandbox modeling experiments were conducted to reconstruct the basin’s prototype and to verify the structural kinematics inferred from the seismic data. Integration of seismic interpretation, physical modeling, and thin-section microstructural observations of fault-related cores allowed us to establish a comprehensive Cenozoic evolutionary model of the sag. The results reveal three main tectonic evolution stages: (1) an extensional fault-depression stage during the Shahejie period, (2) a strike-slip modification phase during the Dongying period, and (3) a subsequent thermal-subsidence stage in the Guantao period. Pre-existing basement faults exerted a significant control on fault geometry, subsidence patterns, and the segmentation of four sub-sags. Moreover, transtensional and transpressional deformation during the late stages reshaped the basin architecture and fault linkage systems. These findings provide new insights into the structural evolution and controlling mechanisms of the Eastern Sag, offering valuable guidance for deep hydrocarbon exploration in the Bohai Bay Basin. Full article

In the domain of automatic parking systems, parking space detection and localization represent fundamental challenges that must be addressed. As a core research focus within the field of intelligent automatic parking, they constitute the essential prerequisite for the realization of fully autonomous parking. Accurate and effective detection of parking spaces is still the core problem that needs to be solved in automatic parking systems. In this study, building upon existing public parking space datasets, a comprehensive panoramic parking space dataset named PSEX (Parking Slot Extended) with complex environmental diversity was constructed by integrating the concept of GAN (Generative Adversarial Network)-based image style transfer. Meanwhile, an improved algorithm based on PP-Yoloe (Paddle-Paddle Yoloe) is used to detect the state (free or occupied) and angle (T-shaped or L-shaped) of the parking space in real-time. For the many and small labels of the parking space, the ResSpp in it is replaced by the ResSimSppf module, the SimSppf structure is introduced at the neck end, and Silu is replaced by Relu in the basic structure of the CBS (Conv-BN-SiLU), and finally an auxiliary detector head is added at the prediction head. Experimental results show that the proposed SimSppf_mepre-Yoloe model achieves an average improvement of 4.5% in mAP50 and 2.95% in mAP50:95 over the baseline PP-Yoloe across various parking space detection tasks. In terms of efficiency, the model maintains comparable inference latency with the baseline, reaching up to 33.7 FPS on the Jetson AGX Xavier platform under TensorRT optimization. And the improved enhancement algorithm can greatly enrich the diversity of parking space data. These results demonstrate that the proposed model achieves a better balance between detection accuracy and real-time performance, making it suitable for deployment in intelligent vehicle and robotic perception systems. Full article

The appearance of spin-induced ferroelectric polarization in the so-called type-II multiferroic materials has received a lot of attention. The nature and mechanisms of such polarization were intensively studied using perovskite rare-earth manganites, RMnO₃, as model systems. Later, multiferroic properties were discovered in some RFeO₃ perovskites and possibly in some RCrO₃ perovskites. However, R₂NiMnO₆ double perovskites have ferromagnetic structures that do not break the inversion symmetry. It was found recently that more complex magnetic structures are realized in A-site-ordered quadruple perovskites, RMn₃Ni₂Mn₂O₁₂. Therefore, they have the potential to be multiferroics. In this work, dielectric properties in magnetic fields up to 9 T were investigated for such perovskites as RMn₃Ni₂Mn₂O₁₂ with R = Ce to Ho and for BiMn₃Ni₂Mn₂O₁₂. The samples with R = Bi, Ce, and Nd showed no dielectric anomalies at all magnetic fields, and the dielectric constant decreases with decreasing temperature. The samples with R = Sm to Ho showed qualitatively different behavior when the dielectric constant started increasing with decreasing temperature below certain temperatures close to the magnetic ordering temperatures, T_N. This difference could suggest different magnetic ground states. The samples with R = Eu, Dy, and Ho still showed no anomalies on the dielectric constant. On the other hand, peaks emerged at T_N on the dielectric constant in the R = Sm sample from about 2 T up to the maximum available field of 9 T. The Gd sample showed peaks on dielectric constant at T_N between about 1 T and 7 T. Transition temperatures increase with increasing magnetic fields for R = Sm and decrease for R = Gd. These findings suggest the presence of magnetic-field-induced multiferroic states in the R = Sm and Gd samples with intermediate ionic radii. Dielectric properties at different magnetic fields are also reported for Lu₂NiMnO₆ for comparison. Full article

Monitoring traffic flow across large-scale transportation networks is essential for effective traffic management, yet comprehensive sensor deployment is often infeasible due to financial and practical constraints. The traffic sensor location problem (TSLP) aims to determine the minimal set of sensor placements needed to achieve full link flow observability. Existing solutions primarily rely on algebraic or optimization-based approaches, but often neglect the impact of sensor measurement errors and struggle with scalability in large, complex networks. This study proposes a new scalable and robust methodology for solving the TSLP under uncertainty, incorporating a formulation that explicitly models the propagation of measurement errors in sensor data. Two nonlinear integer optimization models, Min-Max and Min-Sum, are developed to minimize the inference error across the network. To solve these models efficiently, we introduce the BBA Algorithm (BBA) as an adaptive metaheuristic optimizer, not as a subject of comparative study, but as an enabler of scalability within the proposed framework. The methodology integrates LU decomposition for efficient matrix inversion and employs a node-based flow inference technique that ensures observability without requiring full path enumeration. Tested on benchmark and real-world networks (e.g., fishbone, Sioux Falls, Barcelona), the proposed framework demonstrates strong performance in minimizing error and maintaining scalability, highlighting its practical applicability for resilient traffic monitoring system design. Full article

One of the essential parts of contemporary cryptographic systems is s-boxes (Substitution Boxes), which give encryption algorithms more complexity and resilience due to their nonlinear structure. In this study, we propose CSBA (Cuckoo Search–Bees Algorithm), a hybrid evolutionary method that combines the strengths of Cuckoo Search and Bees algorithms, to generate s-box structures with strong cryptographic properties. The initial population is generated with a high-diversity four-dimensional Memristive Lu chaotic map, taking advantage of the random yet deterministic nature of chaotic systems. This proposed method was designed with inspiration from biological systems. It was developed based on the foraging strategies of bees and the reproductive strategies of cuckoos. This nature-inspired structure enables an efficient scanning of the solution space. The resultant s-boxes’ fitness was assessed using the nonlinearity value. These s-boxes were then optimized using the hybrid CSBA algorithm suggested in this paper as well as the Bees algorithm. The performance of the proposed approaches was measured using SAC, nonlinearity, BIC-SAC, BIC-NL, maximum difference distribution, and linear uniformity (LU) metrics. Compared to other studies in the literature that used metaheuristic algorithms to generate s-boxes, the proposed approach demonstrates good performance. In particular, the average value of 109.75 obtained for the nonlinearity metric demonstrates high success. Therefore, this study demonstrates that robust and reliable s-boxes can be generated for symmetric encryption algorithms using the developed metaheuristic algorithms. Full article

Micro-motors are essential for power drive systems, and efficient fault diagnosis is crucial to reduce safety risks and economic losses caused by failures. However, the fault signals from micro-motors typically exhibit weak and unclear characteristics. To address this challenge, this paper proposes a novel fault diagnosis method that integrates jump plus AM-FM mode decomposition (JMD), symmetrized dot pattern (SDP) visualization, and an improved convolutional neural network (ICNN). Firstly, we employed the jump plus AM-FM mode decomposition technique to decompose the mixed fault signals, addressing the problem of mode mixing in traditional decomposition methods. Then, the intrinsic mode functions (IMFs) decomposed by JMD serve as the multi-channel inputs for symmetrized dot pattern, constructing a two-dimensional polar coordinate petal image. This process achieves both signal reconstruction and visual enhancement of fault features simultaneously. Finally, this paper designed an ICNN method with LeakyReLU activation function to address the vanishing gradient problem and enhance classification accuracy and training efficiency for fault diagnosis. Experimental results indicate that the proposed JMD-SDP-ICNN method outperforms traditional methods with a significantly superior fault classification accuracy of up to 99.2381%. It can offer a potential solution for the monitoring of electromechanical structures under complex conditions. Full article