Search Results (24,140)

The treatment of moving objects in simultaneous localization and mapping (SLAM) is a key challenge in contemporary robotics. In this paper, we propose an extension of the EKF-SLAM algorithm that incorporates moving objects into the estimation process, which we term KISS. We have extended the robotic vision toolbox to analyze the influence of moving objects in simulations. Two linear and one nonlinear motion models are used to represent the moving objects. The observation model remains the same for all objects. The proposed model is evaluated against an implementation of the state-of-the-art formulation for moving object tracking, DATMO. We investigate increasing numbers of static landmarks and dynamic objects to demonstrate the impact on the algorithm and compare it with cases where a moving object is mistakenly integrated as a static landmark (false negative) and a static landmark as a moving object (false positive). In practice, distances to dynamic objects are important, and we propose the safety–distance–error metric to evaluate the difference between the true and estimated distances to a dynamic object. The results show that false positives have a negligible impact on map distortion and ATE with increasing static landmarks, while false negatives significantly distort maps and degrade performance metrics. Explicitly modeling dynamic objects not only performs comparably in terms of map distortion and ATE but also enables more accurate tracking of dynamic objects with a lower safety–distance–error than DATMO. We recommend that researchers model objects with uncertain motion using a simple constant position model, hence we name our contribution Keep it Static SLAMMOT. We hope this work will provide valuable data points and insights for future research into integrating moving objects into SLAM algorithms. Full article

Industrial computed tomography (CT) is widely used in the measurement field owing to its advantages such as non-contact and high precision. To obtain accurate size parameters, fitting parameters can be obtained rapidly by processing volume data in the form of point clouds. However, due to factors such as artifacts in the CT reconstruction process, many abnormal interference points exist in the point clouds obtained after segmentation. The classic least squares algorithm is easily affected by these points, resulting in significant deviation of the solution of linear equations from the normal value and poor robustness, while the random sample consensus (RANSAC) approach has insufficient fitting accuracy within a limited timeframe and the number of iterations. To address these shortcomings, we propose a spherical point cloud fitting algorithm based on projection filtering and K-Means clustering (PK-RANSAC), which strategically integrates and enhances these two methods to achieve excellent accuracy and robustness. The proposed method first uses RANSAC for rough parameter estimation, then corrects the deviation of the spherical center coordinates through two-dimensional projection, and finally obtains the spherical center point set by sampling and performing K-Means clustering. The largest cluster is weighted to obtain accurate fitting parameters. We conducted a comparative experiment using a three-dimensional ball-plate standard. The sphere center fitting deviation of PK-RANSAC was 1.91 µm, which is significantly better than RANSAC’s value of 25.41 µm. The experimental results demonstrate that PK-RANSAC has higher accuracy and stronger robustness for fitting geometric parameters. Full article

This work presents a novel ΣΔ analog-to-digital converter (ADC) architecture for a high-resolution sensor interface. The concept is to reduce the effect of kT/C noise generated by the loop filter by placing the gain stage in front of the loop filter. The proposed architecture effectively reduces the kT/C noise power from the loop filter by as much as the squared gain of the added gain stage. The gain stage greatly relaxes the loop filter’s sampling capacitor requirements. The target resolution is 20 bit. The sampling frequency is 512 kHz, and the oversampling ratio (OSR) is only 256 for a target resolution. Therefore, the proposed ΔΣ ADC structure allows for high-resolution ADC design in an environment with a limited OSR. The proposed ADC designed in 65 nm CMOS technology operates at supply voltages of 1.2 V and achieves a peak signal-to-noise ratio (SNR) and Schreier Figure of Merit (FoMs) of 117.7 dB and 180.4 dB, respectively. Full article

Previous studies have shown that post-thaw sperm performance is affected by multiple stressors during cryopreservation, such as those induced by physical, chemical, mechanical and physiological changes. One of these is the balance disturbance between the antioxidant defense system and reactive oxygen species (ROS) production. This study investigated whether this disturbance could be alleviated by the addition of different antioxidants to cryoprotective solution [8% dimethyl sulfoxide (DMSO) in 1 µm filtered seawater] optimized for the sperm in dwarf surf clam Mulinia lateralis, the model bivalve species used in many different types of studies. Results showed that the addition of 20 μM coenzyme Q10 (Q10) to 8% DMSO achieved a D-stage larval rate similar to that of the fresh control at a sperm-to-egg ratio at least 50% less than the 8% DMSO treatment alone. The addition of other antioxidants (glycine, melatonin and polyvinylpyrrolidone) did not have any positive effects. The improvement in post-thaw sperm quality by Q10 could be due to its ability to significantly decrease ROS production and lipid peroxidation and significantly increase the motility, plasma membrane integrity, mitochondrial membrane potential, acrosome integrity, DNA integrity and activities of catalase and glutatione. In this study, 37 fatty acids (FAs) were quantified in dwarf surf clam sperm, with 21 FAs being significantly impacted by the cryopreservation with 8% DMSO. Thirteen of these 21 FAs were changed due to the addition of 20 μM Q10 to 8% DMSO, with approximately half of them being improved significantly toward the levels of fresh control, while the remaining half extended further from the trends shown with 8% DMSO treatment. However, no significant difference was found in the percentage of each FA category sum and the ratio of unsaturated/saturated FAs between the two treated groups. In conclusion, the antioxidant Q10 has shown the potential to further improve the sperm cryopreservation technique in bivalves. Full article

Hyperbolic metamaterials (HMMs) are highly anisotropic materials with the unique property of generating electromagnetic modes. Understanding how these materials can be applied to control the propagation of light waves remains a major focus in photonics. In this study, we inserted a finite-size HMM rod into the point defect of two-dimensional photonic crystals (PhCs) and investigated the unique cavity modes of this hybrid system. The HMM enhances the efficiency of the cavity system in controlling light transmission. Numerical results demonstrate that the cavity modes based on HMMs can be categorized into various types, showing high Q-factors and promising potential for resonant modulation. Furthermore, the switching performance of the cavity with an HMM rod was examined, revealing that the finite-size HMM modes are highly frequency-sensitive and suitable for nonlinear controlled all-optical switching. These switches, characterized by low power consumption and high extinction ratios, are highly suitable for integration into photonic systems. Our investigation on the new type of HMM cavity illustrates that anisotropic materials can be effectively applied in cavity systems to generate highly efficient modes for filtering and switching. Full article

The current study proposed and evaluated “residual squeeze and excitation attention gate” (rSEAG), a novel network that can improve image quality by reducing distortion attributed to artifacts. This method was established by modifying the Cycle Generative Adversarial Network (cycleGAN)-based generator network using projection data for pre-reconstruction processing in digital breast tomosynthesis. Residual squeeze and excitation were installed in the bridge of the generator network, and the attention gate was installed in the skip connection between the encoder and decoder. Based on the radiation dose index (exposure index and division index) incident on the detector, the cases approved by the ethics committee and used for the study were classified as reference (675 projection images) and object (675 projection images). For the cases, unsupervised data containing a mixture of cases with and without masses were used. The cases were trained using cycleGAN with rSEAG and the conventional networks (ResUNet and U-Net). For testing, predictive processing was performed on cases (60 projection images) that were not used for learning. Images were generated using filtered backprojection reconstruction (kernel: Ramachandran and Lakshminarayanan) from projection data for testing data and without pre-reconstruction processing data (evaluation: in-focus plane). The distortion was evaluated using perception-based image quality evaluation (PIQE) analysis, texture analysis (feature: “Homogeneity” and “Contrast”), and a statistical model with a Gumbel distribution. PIQE has a low rSEAG value. Texture analysis showed that rSEAG and a network without cycleGAN were similar in terms of the “Contrast” feature. In dense breasts, ResUNet had the lowest “Contrast” feature and U-Net had differences between cases. The maximal variations in the Gumbel plot, rSEAG reduced the high-frequency ripple artifacts. In this study, rSEAG could improve distortion and reduce ripple artifacts. Full article

Traditional deep learning methods employing 2D images can only classify healthy and unhealthy seedlings; consequently, this study proposes a method by which to further classify healthy seedlings into primary seedlings and secondary seedlings and finally to differentiate three classes of seedling through a 3D point cloud for the detection of useful eggplant seedling transplants. Initially, RGB images of three types of substrate-cultivated eggplant seedlings (primary, secondary, and unhealthy) were collected, and healthy and unhealthy seedlings were classified using ResNet50, VGG16, and MobilNetV2. Subsequently, a 3D point cloud was generated for the three seedling types, and a series of filtering processes (fast Euclidean clustering, point cloud filtering, and voxel filtering) were employed to remove noise. Parameters (number of leaves, plant height, and stem diameter) extracted from the point cloud were found to be highly correlated with the manually measured values. The box plot shows that the primary and secondary seedlings were clearly differentiated for the extracted parameters. The point clouds of the three seedling types were ultimately classified directly using the 3D classification models PointNet++, dynamic graph convolutional neural network (DGCNN), and PointConv, in addition to the point cloud complementary operation for plants with missing leaves. The PointConv model demonstrated the best performance, with an average accuracy, precision, and recall of 95.83, 95.83, and 95.88%, respectively, and a model loss of 0.01. This method employs spatial feature information to analyse different seedling categories more effectively than two-dimensional (2D) image classification and three-dimensional (3D) feature extraction methods. However, there is a paucity of studies applying 3D classification methods to predict useful eggplant seedling transplants. Consequently, this method has the potential to identify different eggplant seedling types with high accuracy. Furthermore, it enables the quality inspection of seedlings during agricultural production. Full article

In the complex clutter background, the clutter center frequency is not fixed, and the spectral width is wide, which leads to the performance degradation of the traditional adaptive clutter suppression method. Therefore, an adaptive clutter intelligent suppression method based on deep reinforcement learning (DRL) is proposed. Each range cell to be detected is regarded as an independent intelligence (agent) in the proposed method. The clutter environment is interactively learned using a deep learning (DL) process, and the filter parameter optimization is positively motivated by the reinforcement learning (RL) process to achieve the best clutter suppression effect. The suppression performance of the proposed method is tested on simulated and real data. The experimental results indicate that the filter notch designed by the proposed method is highly matched with the clutter compared with the existing adaptive clutter suppression methods. While suppressing the clutter, it has a higher amplitude-frequency response to signals at non-clutter frequencies, thus reducing the loss of the target signal and maximizing the output signal-to-clutter and noise rate (SCNR). Full article

Phosphorus (P) discharge from anthropogenic sources, notably sewage effluent and agricultural runoff, significantly contributes to eutrophication in aquatic environments. Stringent regulations have heightened the need for effective P removal technologies in wastewater treatment processes. This paper provides a comprehensive review of current P removal methods, focusing on both biological and chemical approaches. Biological treatments discussed include enhanced biological P removal in activated sludge systems, biological trickling filters, biofilm reactors, and constructed wetlands. The efficiency of microbial absorption and novel biotechnological integrations, such as the use of microalgae and fungi, are also examined. Chemical treatments reviewed encompass the application of metal salts, advanced oxidation processes such as chlorination, ozonation, and the Fenton reaction, as well as emerging techniques including the Electro-Fenton process and photocatalysis. Analytical methods for P, including spectrophotometric techniques and fractionation analyses, are evaluated to understand the dynamics of P in wastewater. This review critically assesses the strengths and limitations of each method, aiming to identify the most effective and sustainable solutions for P management in wastewater treatment. The integration of innovative strategies and advanced technologies is emphasized as crucial for optimizing P removal and ensuring compliance with environmental regulations. Full article

Bacterial microbiomes influence global carbon and nutrient cycling as the environment changes. Rain-fed rock basins are ephemeral aquatic systems, potentially subject to extreme environmental stress, that can host a wide variety of biological communities, including bacteria. However, bacterial communities are barely described in these habitats. Here we provide a detailed description on the occurrence, diversity and distribution patterns of the bacterial communities within and between rain-fed granite mountain rock basins located in the Sierra de Guadarrama National Park, Spain, using high-throughput sequencing of 16S RNA. We recovered a highly diverse community consisting of 3174 operational taxonomic units (OTUs) belonging to 32 phyla. In total, 50% of OTUs were shared among basins and 6–10% were basin-exclusive OTUs, suggesting a robust global bacterial metacommunity colonizes the basins. The existence of 6% replicate-exclusive OTUs and the fact that at least four replicates were required to catalogue 90% of the basin bacterial community emphasized the heterogeneity of these habitats. Both environmental filtering and random dispersal are likely to be involved in the arrangement of the bacterial communities. The taxa identified in this study are versatile in metabolism, and some have biotechnological potential. The taxonomic affiliation of many of the OTUs found suggests that rain-fed rock basins could be a resource for mining novel bacterial biocompounds. Full article

In an ultrasonic system, a piezoelectric transducer (PT) is a key component and contains a piezo element inside. In order to design and operate a system that uses a piezo element for its intended purpose, resonance analysis of the piezo element and an equivalent circuit analysis of the output stage of the ultrasonic system generator are required. Due to the characteristics of the equivalent circuit, a piezo element has multiple resonance points. Therefore, the system must be operated at the corresponding frequency by tracking the resonance frequency that suits the purpose of the system. In this study, the mechanical resonance frequency of the piezo element was analyzed and a method for operating the system at the corresponding frequency was studied. In order to operate a piezo element, a voltage-type inverter is used to apply a high-frequency AC (Alternating Current). Then, an LC filter is inserted into the output stage of the inverter, and the piezo element is finally located at the output stage of the LC filter. Therefore, when designing an LC filter, a design is required to optimize the performance of the piezo element. In this paper, we analyzed the resonance of a piezo element and the equivalent circuit of the generator output stage of an ultrasonic system for effective operation of an ultrasonic system. In addition, we proposed a method to estimate the characteristics of the entire mechanical resonance frequency range of a piezo element by using an LSTM (Long Short-Term Memory) model suitable for analyzing the nonlinear characteristics of a piezo element. The study on estimating the mechanical resonance frequency of a piezo element using an LSTM model was verified through MATLAB 2021b simulation and ultrasonic system experiments. Full article

This study aimed to determine whether using DNA-based markers assigned to individual chromosomes would detect the genetic structures of 446 winter triticale forms originating from two breeding companies more effectively than using the entire pool of markers. After filtering for quality control parameters, 6380 codominant single nucleotide polymorphisms (SNPs) markers and 17,490 dominant diversity array technology (silicoDArT) markers were considered for analysis. The mean polymorphic information content (PIC) values varied depending on the chromosomes and ranged from 0.30 (2R) to 0.43 (7A) for the SNPs and from 0.28 (2A) to 0.35 (6R) for the silicoDArTs. The highest correlation of genetic distance (GD) matrices based on SNP markers was observed among the 5B–5R (0.642), 5B–7B (0.626), and 5A–5R (0.605) chromosomes. When silicoDArTs were used for the analysis, the strongest correlations were found between 5B–5R (0.732) and 2B–5B (0.718). A Bayesian analysis showed that SNPs (total marker pool) allowed for the identification of a more complex structure (K = 4, ΔK = 2460.2) than the analysis based on silicoDArTs (K = 2, ΔK = 128). Triticale lines formed into groups, ranging from two (most of the chromosomes) to four (7A) groups depending on the analyzed chromosome when SNP markers were used for analysis. Linkage disequilibrium (LD) varied among individual chromosomes, ranging from 0.031 for 1A to 0.228 for 7R. Full article

The standard Kalman filter and most of its enhancements are typically designed based on the criterion that minimizes the mean squared error, with little discussion of multiple criteria in the positioning and navigation fields. Therefore, a novel data fusion method that takes into account weighted multiple criteria is proposed in this paper, implementing a filtering algorithm based on integrated criteria with different weights determined by a weight adjustment factor. The proposed algorithm and conventional filtering algorithms were utilized for data fusion in GNSS/INS integration. Experiments were conducted using actual data collected from an urban environment. Comparative analysis revealed that, when utilizing the proposed algorithm, the precision of the position, velocity, and attitude of the tested land vehicle could be improved by approximately 24%, 48%, and 35%, respectively. Furthermore, a series of filtering algorithms with different weight adjustment factors was performed to test their influence on the filtering. The application of the proposed algorithm should be accompanied by an appropriate weight adjustment factor. Full article

In the process of scratch defect detection in textured materials, there are often problems of low efficiency in traditional manual detection, large errors in machine vision, and difficulty in distinguishing defective scratches from the background texture. In order to solve these problems, we developed an enhanced scratch defect detection system for textured materials based on optimized photometric stereo vision and FFT-Gabor filtering. We designed and optimized a novel hemispherical image acquisition device that allows for selective lighting angles. This device integrates images captured under multiple light sources to obtain richer surface gradient information for textured materials, overcoming issues caused by high reflections or dark shadows under a single light source angle. At the same time, for the textured material, scratches and a textured background are difficult to distinguish; therefore, we introduced a Gabor filter-based convolution kernel, leveraging the fast Fourier transform (FFT), to perform convolution operations and spatial domain phase subtraction. This process effectively enhances the defect information while suppressing the textured background. The effectiveness and superiority of the proposed method were validated through material applicability experiments and comparative method evaluations using a variety of textured material samples. The results demonstrated a stable scratch capture success rate of 100% and a recognition detection success rate of 98.43% ± 1.0%. Full article

When analyzing GPS time series, common mode errors (CME) often obscure the actual crustal movement signals, leading to deviations in the velocity estimates of station coordinates. Therefore, mitigating the impact of CME on station positioning accuracy is crucial to ensuring the precision and reliability of GNSS time series. The current approach to separating CME mainly uses signal filtering methods to decompose the residuals of the observation network into multiple signals, from which the signals corresponding to CME are identified and separated. However, this method overlooks the spatial correlation of the stations. In this paper, we improved the Independent Component Analysis (ICA) method by introducing correlation coefficients as weighting factors, allowing for more accurate emphasis or attenuation of the contributions of the GNSS network’s spatial distribution during the ICA process. The results show that the improved Weighted Independent Component Analysis (WICA) method can reduce the root mean square (RMS) of the coordinate time series by an average of 27.96%, 15.23%, and 28.33% in the E, N, and U components, respectively. Compared to the ICA method, considering the spatial distribution correlation of stations, the improved WICA method shows enhancements of 12.53%, 3.70%, and 8.97% in the E, N, and U directions, respectively. This demonstrates the effectiveness of the WICA method in separating CMEs and provides a new algorithmic approach for CME separation methods. Full article