Search Results (1,171)

The technological advances and material control that have resulted from reductive and deterministic practices of science are quite real. The digitally mediated expansion of experiential freedoms-of-choice and the transformative problem-solving potential of artificial intelligence are undeniable. But for all its successes, reductive physicalism has failed to solve the so-called hard problem of consciousness. As a result, its successes are exposing humanity to an intensifying confluence of existential and ethical risks as the digitally mediated attention economy and intelligent technology facilitate a fundamental restructuring of the dynamics of human presence. Making use of Buddhist conceptual resources and drawing out their implications regarding causality and agency, this paper offers a nondualist and nonreductionist approach to theorizing consciousness and evolutionary dynamics in ways that are suited to opening an ethically productive “middle path” to critically rethinking the so-called Fourth Industrial Revolution and more positively configuring the evolution human–technology–world relations. Full article

This paper presents a method for designing a static output feedback integrated path tracking controller for autonomous vehicles. For path tracking, state–space model-based control methods, such as linear quadratic regulator, H_∞ control, sliding mode control, and model predictive control, have been selected as controller design methodologies. However, these methods adopt full-state feedback. Among the state variables, the lateral velocity, or the side-slip angle, is hard to measure in real vehicles. To cope with this problem, it is desirable to use a state estimator or static output feedback (SOF) control. In this paper, an SOF control is selected as the controller structure. To design the SOF controller, a linear quadratic optimal control and sliding mode control are adopted as controller design methodologies. Front wheel steering (FWS), rear wheel steering (RWS), four-wheel steering (4WS), four-wheel independent braking (4WIB), and driving (4WID) are adopted as actuators for path tracking and integrated as several actuator configurations. For better performance, a lookahead or preview function is introduced into the state–space model built for path tracking. To verify the performance of the SOF path tracking controller, simulations are conducted on vehicle simulation software. From the simulation results, it is shown that the SOF path tracking controller presented in this paper is effective for path tracking with limited sensor outputs. Full article

In recent years, deep-learning-based speech synthesis has garnered substantial attention, achieving remarkable advancements in generating human-like speech. However, synthesized speech often lacks naturalness, primarily because models excessively depend on fine-grained text–speech alignment. To address this issue, we propose MixDiff-TTS, a novel non-autoregressive model. MixDiff-TTS incorporates a linguistic encoder based on a mixture alignment mechanism, which combines word-level hard alignment with phoneme-level soft alignment. This design reduces reliance on fine-grained alignment, enabling the model to handle ambiguous phonetic boundaries more robustly. Additionally, we introduce a Word-to-Phoneme Attention module with a relative position bias mechanism to improve the model’s capacity for processing long text sequences. We evaluate the performance of MixDiff-TTS on the LJSpeech dataset. The experimental results show that MixDiff-TTS scores 0.507 for SSIM (Structural Similarity Index) and 6.652 for MCD (Mel Cepstral Distortion). This suggests that the synthesized speech is closer to real speech in spectral structure and exhibits lower spectral distortion than state-of-the-art baselines (such as FastSpeech2 and DiffSpeech). MixDiff-TTS also achieves a MOS (Mean Opinion Score) of 3.95, which is close to that of real speech. These results indicate that MixDiff-TTS can synthesize speech with high naturalness and quality. Ablation studies demonstrate the effectiveness of our method. Full article

Tree crown detection of high-resolution UAV forest remote sensing images using computer technology has been widely performed in the last ten years. In forest resource inventory management based on remote sensing data, crown detection is the most important and essential part. Deep learning technology has achieved good results in tree crown segmentation and species classification, but relying on high-performance computing platforms, edge calculation, and real-time processing cannot be realized. In this thesis, the UAV images of coniferous Pinus tabuliformis and broad-leaved Salix matsudana collected by Jingyue Ecological Forest Farm in Changping District, Beijing, are used as datasets, and a lightweight neural network U-Net-Light based on U-Net and VGG16 is designed and trained. At the same time, the IP core and SoC architecture of the neural network accelerator are designed and implemented on the Xilinx ZYNQ 7100 SoC platform. The results show that U-Net-light only uses 1.56 MB parameters to classify and segment the crown images of double tree species, and the accuracy rate reaches 85%. The designed SoC architecture and accelerator IP core achieved 31 times the speedup of the ZYNQ hard core, and 1.3 times the speedup compared with the high-end CPU (Intel CoreTM i9-10900K). The hardware resource overhead is less than 20% of the total deployment platform, and the total on-chip power consumption is 2.127 W. Shorter prediction time and higher energy consumption ratio prove the effectiveness and rationality of architecture design and IP development. This work departs from conventional canopy segmentation methods that rely heavily on ground-based high-performance computing. Instead, it proposes a lightweight neural network model deployed on FPGA for real-time inference on unmanned aerial vehicles (UAVs), thereby significantly lowering both latency and system resource consumption. The proposed approach demonstrates a certain degree of innovation and provides meaningful references for the automation and intelligent development of forest resource monitoring and precision agriculture. Full article

Taking the plant as a total uncertainty in a black box with measurable inputs and attainable outputs, this paper presents a constructive control design of agnostic nonlinear dynamic systems with discontinuous input (such as hard nonlinearities in the forms of dead zones, friction, and backlashes). This study expands the model-free sliding mode control (MFSMC), based on the Lyapunov differential inequality, to a total model-free robust control (TMFRC) for this class of piecewise systems, which does not use extra adaptive online data fitting modelling to deal with plant uncertainties and input discontinuities. The associated properties are analysed to justify the constraints and provide assurance for system stability analysis. Numerical examples in control of a non-affine nonlinear plant with three hard nonlinear inputs—a dead zone, Coulomb and viscous friction, and backlash—are used to test the feasibility of the TMFRC. Furthermore, real experimental tests on a permanent magnet synchronous motor (PMSM) are also given to showcase the control’s applicability and offer guidance for implementation. Full article

Accurate 3D object detection is crucial for autonomous vehicles (AVs) to navigate safely in complex environments. This paper introduces a novel fusion framework that integrates Camera image-based 2D object detection using YOLOv8 and LiDAR data-based 3D object detection using PointPillars, hence named C2L3-Fusion. Unlike conventional fusion approaches, which often struggle with feature misalignment, C2L3-Fusion enhances spatial consistency and multi-level feature aggregation, significantly improving detection accuracy. Our method outperforms state-of-the-art approaches such as YoPi-CLOCs Fusion Network, standalone YOLOv8, and standalone PointPillars, achieving mean Average Precision (mAP) scores of 89.91% (easy), 79.26% (moderate), and 78.01% (hard) on the KITTI dataset. Successfully implemented on the Nvidia Jetson AGX Xavier embedded platform, C2L3-Fusion maintains real-time performance while enhancing robustness, making it highly suitable for self-driving vehicles. This paper details the methodology, mathematical formulations, algorithmic advancements, and real-world testing of C2L3-Fusion, offering a comprehensive solution for 3D object detection in autonomous navigation. Full article

Mineral scaling and corrosion pose significant challenges in groundwater distribution, increasing hydraulic resistance, reducing flow rates, and raising operational costs. Magnetic water treatment (MWT) has gained attention as a non-chemical method to mitigate scale formation by promoting the transformation of calcite, a hard and adherent CaCO₃ polymorph, into aragonite, a softer and less adherent form. In Chihuahua, Mexico, mineral scaling has disrupted the drinking water distribution system, reducing flow and impairing service. This study evaluates MWT’s potential to mitigate scaling by analyzing magnetized water treated under various MWT configurations. Comparative analyses were conducted via XRD and SEM to assess changes in calcium carbonate polymorphs. Finite element method (FEM) simulations in COMSOL Multiphysics 6.0 were used to evaluate the magnetic field distribution. The results show no systematic trend in CaCO₃ polymorph transformation following MWT exposure, and FEM simulations indicate negligible magnetic field gradients in certain configurations. These findings highlight the critical role of optimizing magnetic field alignment and gradient strength. Future research should refine MWT configurations and incorporate real-time monitoring to enhance its effectiveness in scale prevention. Full article

Accurately estimating the State of Charge (SOC) of power batteries is crucial for the Battery Management Systems (BMS) in new energy intelligent connected vehicles. It directly influences vehicle range, energy management efficiency, and the safety and lifespan of the battery. However, SOC cannot be measured directly with instruments; it needs to be estimated using external parameters such as current, voltage, and internal resistance. Moreover, power batteries represent complex nonlinear time-varying systems, and various uncertainties—like battery aging, fluctuations in ambient temperature, and self-discharge effects—complicate the accuracy of these estimations. This significantly increases the complexity of the estimation process and limits industrial applications. To address these challenges, this study systematically classifies existing SOC estimation algorithms, performs comparative analyses of their computational complexity and accuracy, and identifies the inherent limitations within each category. Additionally, a comprehensive review of SOC estimation technologies utilized in BMS by automotive OEMs globally is conducted. The analysis concludes that advancing multi-fusion estimation frameworks, which offer enhanced universality, robustness, and hard real-time capabilities, represents the primary research trajectory in this field. Full article

Early detection of diabetic retinopathy is critical for preserving vision in diabetic patients. The classification of lesions in Retinal fundus images, particularly macular edema, is an essential diagnostic tool, yet it presents a significant learning curve for both novice and experienced ophthalmologists. To address this challenge, a novel Convolutional Deep Belief Network (CDBN) is proposed to classify image patches into three distinct categories: two types of macular edema—microhemorrhages and hard exudates—and a healthy category. The method leverages high-level feature extraction to mitigate issues arising from the high similarity of low-level features in noisy images. Additionally, a Real-Coded Genetic Algorithm optimizes the parameters of Gabor filters and the network, ensuring optimal feature extraction and classification performance. Experimental results demonstrate that the proposed CDBN outperforms comparative models, achieving an F1 score of 0.9258. These results indicate that the architecture effectively overcomes the challenges of lesion classification in retinal images, offering a robust tool for clinical application and paving the way for advanced clinical decision support systems in diabetic retinopathy management. Full article

Accurate and efficient white-spot defects detection for the surface of galvanized strip steel is one of the most important guarantees for the quality of steel production. It is a fundamental but “hard” small target detection problem due to its small pixel occupation in low-contrast images. By fully exploiting the low-rank and sparse prior information of a surface defect image, a Schatten-p norm-based low-rank tensor decomposition (SLRTD) method is proposed to decompose the defect image into low-rank background, sparse defect, and random noise. Firstly, the original defect images are transformed into a new patch-based tensor mode through data reconstruction for mining valuable information of the defect image. Then, considering the over-shrinkage problem in the low-rank component estimation caused by a vanilla nuclear norm and a weighted nuclear norm, a nonlinear reweighting strategy based on a Schatten $p$-norm is incorporated to improve the decomposition performance. Finally, a solution framework is proposed via a well-designed alternating direction method of multipliers to obtain the white-spot defect target image by a simple segmenting algorithm. The white-spot defect dataset from a real-world galvanized strip steel production line is constructed, and the experimental results demonstrate that the proposed SLRTD method outperforms existing state-of-the-art methods qualitatively and quantitatively. Full article

This study explores the design and performance of an underwater magnetic sensor network (UMSN) tailored for intrusion detection in complex environments such as riverbeds and areas with dense vegetation. The system utilizes wireless sensor network (WSN) principles and integrates AMR-based magnetic sensors (e.g., LSM303AGR) with MEMS-based accelerometers to provide accurate and high-resolution magnetic field measurements. Extensive calibration techniques were employed to correct hard-iron and soft-iron distortions, ensuring reliable performance in fluctuating environmental conditions. Field tests included both controlled setups and real-world scenarios, such as detecting intrusions across river sections, shorelines, and coordinated land-water activities. The results showed detection rates consistently above 90%, with response times averaging 2.5 s and a maximum detection range of 5 m. The system also performed well under adverse weather conditions, including fog and rain, demonstrating its adaptability. The findings underline the potential of UMSN as a scalable and cost-efficient solution for monitoring sensitive areas. By addressing the limitations of traditional surveillance systems, this research offers a practical framework for enhancing security in critical regions, laying the groundwork for future developments in magnetic sensor technology. Full article

In modern large-scale pig farming, accurately identifying sow estrus and ensuring timely breeding are crucial for maximizing economic benefits. However, the short duration of estrus and the reliance on subjective human judgment pose significant challenges for precise insemination timing. To enable non-contact, automated estrus detection, this study proposes an improved algorithm, Enhanced Context-Attention YOLO (ECA-YOLO), based on YOLOv11. The model utilizes ocular appearance features—eye’s spirit, color, shape, and morphology—across different estrus stages as key indicators. The MSCA module enhances small-object detection efficiency, while the PPA and GAM modules improve feature extraction capabilities. Additionally, the Adaptive Threshold Focal Loss (ATFL) function increases the model’s sensitivity to hard-to-classify samples, enabling accurate estrus stage classification. The model was trained and validated on a dataset comprising 4461 images of sow eyes during estrus and was benchmarked against YOLOv5n, YOLOv7tiny, YOLOv8n, YOLOv10n, YOLOv11n, and Faster R-CNN. Experimental results demonstrate that ECA-YOLO achieves a mean average precision (mAP) of 93.2%, an F1-score of 88.0%, with 5.31M parameters, and FPS reaches 75.53 frames per second, exhibiting superior overall performance. The findings confirm the feasibility of using ocular features for estrus detection and highlight the potential of ECA-YOLO for real-time, accurate monitoring of sow estrus under complex farming conditions. This study lays the groundwork for automated estrus detection in intensive pig farming. Full article

Precision agriculture is necessary for dealing with problems like pest outbreaks, a lack of water, and declining crop health. Manual inspections and broad-spectrum pesticide application are inefficient, time-consuming, and dangerous. New drone photography and IoT sensors offer quick, high-resolution, multimodal agricultural data collecting. Regional diversity, data heterogeneity, and privacy problems make it hard to conclude these data. This study proposes a lightweight, hybrid deep learning architecture called federated LeViT-ResUNet that combines the spatial efficiency of LeViT transformers with ResUNet’s exact pixel-level segmentation to address these issues. The system uses multispectral drone footage and IoT sensor data to identify real-time insect hotspots, crop health, and yield prediction. The dynamic relevance and sparsity-based feature selector (DRS-FS) improves feature ranking and reduces redundancy. Spectral normalization, spatial–temporal alignment, and dimensionality reduction provide reliable input representation. Unlike centralized models, our platform trains over-dispersed client datasets using federated learning to preserve privacy and capture regional trends. A huge, open-access agricultural dataset from varied environmental circumstances was used for simulation experiments. The suggested approach improves on conventional models like ResNet, DenseNet, and the vision transformer with a 98.9% classification accuracy and 99.3% AUC. The LeViT-ResUNet system is scalable and sustainable for privacy-preserving precision agriculture because of its high generalization, low latency, and communication efficiency. This study lays the groundwork for real-time, intelligent agricultural monitoring systems in diverse, resource-constrained farming situations. Full article

Burkholderia gladioli pathovar cocovenenans (BGC) is a highly lethal foodborne pathogen responsible for outbreaks of food poisoning with the highest recorded mortality rates among bacterial foodborne illnesses in China. In this study, the ecological behavior of BGC and its Bongkrekic Acid (BA) production dynamics in fresh wet rice noodles (FWRN) were investigated under isothermal conditions ranging from 4 °C to 37 °C. Growth kinetics were modeled using the Huang, Baranyi, and modified Gompertz primary models, with secondary models (Huang square root model and Ratkowsky square root model) describing the influence of temperature on growth parameters. Among these, the Huang–Huang model combination exhibited the best performance, with a root mean square error (RMSE) of 0.009 and bias factor (B_f) and accuracy factor (A_f) values close to 1. Additionally, we examined the impact of BGC contamination on the quality attributes of FWRN, including pH, color (L*, a*, b*), hardness, and moisture content. The results indicated that BGC growth significantly increased pH and yellowing (b*) values, while changes in texture and moisture were less pronounced. A probabilistic model was further developed to predict BA production under various temperature scenarios, revealing that BA formation was most likely to occur between 24 °C and 30 °C. While this study provides valuable predictive tools for microbial risk assessment and quality control of FWRN, limitations include the exclusion of additional environmental factors such as oxygen and relative humidity, as well as the lack of direct investigation into the degradation behavior of BA. Future research will expand model parameters and include sensory evaluations and advanced microbiological analyses to enhance applicability under real-world storage and transportation conditions. Full article

Adulteration detection or authentication is considered a type of one-class classification (OCC) in chemometrics. An effective OCC model requires representative samples. However, it is challenging to collect representative samples from all over the world. Moreover, it is also very hard to evaluate the representativeness of collected samples. In this study, we blazed a new trail to propose an authentication method to identify adulterated edible oils without building a prediction model beforehand. An authentication method developed by real-time one-class classification modeling, and model population analysis was designed to identify adulterated oils in the market without building a classification model beforehand. The underlying philosophy of the method is that the sum of the absolute centered residual (ACR) of the good model built by only authentic samples is higher than that of the bad model built by authentic and adulterated samples. In detail, a large number of OCC models were built by selecting partial samples out of inspected samples using Monte Carlo sampling. Then, adulterated samples involved in the test of these good models were identified. Taking the inspected samples of avocado oils as an example, as a result, 6 out of 40 avocado oils were identified as adulterated and then validated by chemical markers. The successful identification of avocado oils adulterated with soybean oil, corn oil, or rapeseed oil validated the effectiveness of our method. The proposed method provides a novel idea for oils as well as other high-value food adulteration detection. Full article