Local Information Research Articles

Multi-channel and multi-featured extended orb with gabor filter and non-maximum suppression

This research paper presents an enhanced approach to feature extraction using the Oriented FAST and Rotated BRIEF (ORB) algorithm (Rublee et al., 2014). The proposed method leverages the power of Gabor filters (Mehrotra et al., 1992) in combination with Non-Maximum Suppression (Neuback and Van Gool, 2006) to improve the robustness and efficiency of feature detection in computer vision applications. The process begins with the acquisition of an RGB image, which is then transformed into seven single-channel images representing different color and intensity aspects: red, green, blue, hue, saturation, value, and grayscale. Each single-channel image is independently subjected to Gabor filtering, resulting in seven Gabor-filtered images. These images capture distinct texture and frequency information, enhancing the discriminative power of subsequent feature extraction. Subsequently, the Oriented FAST and Rotated BRIEF (ORB) algorithm is applied to each of the seven Gabor-filtered images to extract key points and descriptors. This step yields a comprehensive set of keypoints (Mallick et al., 2015) and descriptors, capturing rich local feature information across multiple image channels. To further refine the extracted features and eliminate redundant keypoints, Non-Maximum Suppression is employed independently on each single-channel image. This process effectively filters out overlapped and false keypoints, ensuring the selection of only the most salient features for downstream tasks. Experimental results demonstrate the efficacy of the proposed approach in improving feature detection performance compared to traditional methods. The combination of Gabor filtering and Non-Maximum Suppression enhances feature discriminability, robustness to noise, and resistance to image transformations, making it well-suited for various computer vision applications, including object recognition, image matching, and scene understanding.

Integration of feature enhancement technique in Google inception network for breast cancer detection and classification

Breast cancer is a major public health concern, and early detection and classification are essential for improving patient outcomes. However, breast tumors can be difficult to distinguish from benign tumors, leading to high false positive rates in screening. The reason is that both benign and malignant tumors have no consistent shape, are found at the same position, have variable sizes, and have high correlations. The ambiguity of the correlation challenges the computer-aided system, and the inconsistency of morphology challenges an expert in identifying and classifying what is positive and what is negative. Due to this, most of the time, breast cancer screen is prone to false positive rates. This research paper presents the introduction of a feature enhancement method into the Google inception network for breast cancer detection and classification. The proposed model preserves both local and global information, which is important for addressing the variability of breast tumor morphology and their complex correlations. A locally preserving projection transformation function is introduced to retain local information that might be lost in the intermediate output of the inception model. Additionally, transfer learning is used to improve the performance of the proposed model on limited datasets. The proposed model is evaluated on a dataset of ultrasound images and achieves an accuracy of 99.81%, recall of 96.48%, and sensitivity of 93.0%. These results demonstrate the effectiveness of the proposed method for breast cancer detection and classification.

Sample-Adaptive Classification Inference Network

Existing pre-trained models have yielded promising results in terms of computational time reduction. However, these models only focus on pruning simple sentences or less salient words, while neglecting the treatment of relatively complex sentences. It is frequently these sentences that cause the loss of model accuracy. This shows that the adaptation of the existing models is one-sided. To address this issue, in this paper, we propose a sample-adaptive training and inference model. Specifically, complex samples are extracted from the training datasets and a dedicated data augmentation module is trained to extract global and local semantic information of complex samples. During inference, simple samples can exit the model via the Sample Adaptive Exit Mechanism, Normal samples pass through the whole backbone model before inference, while complex samples are processed by the Characteristic Enhancement Module after passing through the backbone model. In this way, all samples are processed adaptively. Our extensive experiments on classification tasks datasets in the field of Natural Language Processing demonstrate that our method enhances model accuracy and reduces model inference time for multiple datasets. Moreover, our method is transferable and can be applied to multiple pre-trained models.

Local Quantum Joint Entropy and Quantum Coherence Information Based on Local Quantum Bernoulli Noise

Local Quantum Joint Entropy and Quantum Coherence Information Based on Local Quantum Bernoulli Noise

A Robust CoS-PVNet Pose Estimation Network in Complex Scenarios

Object 6D pose estimation, as a key technology in applications such as augmented reality (AR), virtual reality (VR), robotics, and autonomous driving, requires the prediction of the 3D position and 3D pose of objects robustly from complex scene images. However, complex environmental factors such as occlusion, noise, weak texture, and lighting changes may affect the accuracy and robustness of object 6D pose estimation. We propose a robust CoS-PVNet (complex scenarios pixel-wise voting network) pose estimation network for complex scenes. By adding a pixel-weight layer based on the PVNet network, more accurate pixel point vectors are selected, and dilated convolution and adaptive weighting strategies are used to capture local and global contextual information of the input feature map. At the same time, the perspective-n-point localization algorithm is used to accurately locate 2D key points to solve the pose of 6D objects, and then, the transformation relationship matrix of 6D pose projection is solved. The research results indicate that on the LineMod and Occlusion LineMod datasets, CoS-PVNet has high accuracy and can achieve stable and robust 6D pose estimation even in complex scenes.

Markov-Embedded Affinity Learning with Connectivity Constraints for Subspace Clustering

Subspace clustering algorithms have demonstrated remarkable success across diverse fields, including object segmentation, gene clustering, and recommendation systems. However, they often face challenges, such as omitting cluster information and the neglect of higher-order neighbor relationships within the data. To address these issues, a novel subspace clustering method named Markov-Embedded Affinity Learning with Connectivity Constraints for Subspace Clustering is proposed. This method seamlessly embeds Markov transition probability information into the self-expression, leveraging a fine-grained neighbor matrix to uncover latent data structures. This matrix preserves crucial high-order local information and complementary details, ensuring a comprehensive understanding of the data. To effectively handle complex nonlinear relationships, the method learns the underlying manifold structure from a cross-order local neighbor graph. Additionally, connectivity constraints are applied to the affinity matrix, enhancing the group structure and further improving the clustering performance. Extensive experiments demonstrate the superiority of this novel method over baseline approaches, validating its effectiveness and practical utility.

SpatialPrompt: spatially aware scalable and accurate tool for spot deconvolution and domain identification in spatial transcriptomics

Efficiently mapping of cell types in situ remains a major challenge in spatial transcriptomics. Most spot deconvolution tools ignore spatial coordinate information and perform extremely slow on large datasets. Here, we introduce SpatialPrompt, a spatially aware and scalable tool for spot deconvolution and domain identification. SpatialPrompt integrates gene expression, spatial location, and single-cell RNA sequencing (scRNA-seq) dataset as reference to accurately infer cell-type proportions of spatial spots. SpatialPrompt uses non-negative ridge regression and graph neural network to efficiently capture local microenvironment information. Our extensive benchmarking analysis on Visium, Slide-seq, and MERFISH datasets demonstrated superior performance of SpatialPrompt over 15 existing tools. On mouse hippocampus dataset, SpatialPrompt achieves spot deconvolution and domain identification within 2 minutes for 50,000 spots. Overall, domain identification using SpatialPrompt was 44 to 150 times faster than existing methods. We build a database housing 40 plus curated scRNA-seq datasets for seamless integration with SpatialPrompt for spot deconvolution.

Dual-weight Attention-based Multi-source Multi-stage Alignment Domain Adaptation for Industrial Fault Diagnosis

Abstract Cross-domain fault diagnosis is crucial for industrial applications with various and unknown operating conditions. However, due to the significant differences in the distribution of features in multiple source domains, it may lead to mutual interference of features between different domains and reduce the accuracy of diagnosis, which is a problem not considered by most current researches. In addition, most of the existing methods focus only on the extraction of low-frequency global information and cannot adequately deal with high-frequency local information. Consequently, this paper provides a multi-stage processing integrated dual-weight attention-based multi-source multi-stage aligned domain adaptation (DAMMADA) method. Global fault features that are shared by various subdomains are extracted by three domain-specific feature extractors from various domains. In a local feature extractor, the dual-weight attention module not only uses shared weights to aggregate local information, but it also uses contextual weights to improve local features. In terms of loss handling, multiple pseudo-labels are used to reduce the loss of the local maximum mean discrepancy (LMMD) in order to learn the domain-invariant characteristics after improving the high-frequency and low-frequency information extraction. To modify the classification boundaries, the pseudo-labels' mean square errors (MSE) are combined. Comprehensive experiments were carried out on two platforms for fault diagnosis of SCARA robots and bearings respectively, and the results demonstrated that DAMMADA is superior to other methods in terms of accuracy and its ability to suppress negative migration for cross-domain tasks.

Decentralized Online Order Fulfillment in Omni-Channel Retailers

We consider an order fulfillment problem of an omni-channel retailer that ships online orders from its distribution center (DC) and brick-and-mortar stores. Stores use their local information, not observed by the retailer, that can lead them to accept or reject fulfillment requests of items in an online order. We investigate the problem of sequencing requests to stores and inventory rationing decisions at the DC to minimize expected costs under uncertain store acceptance behavior and when items are indistinguishable in terms of shipping. First, under the scenario that stores are used only when the DC has insufficient inventory, we propose a Markov Decision Process formulation and analyze the performance of myopic policies that are preferable because of their interpretability. We show that the performance rate of a myopic approach that orders stores by cost only depends on the number of items in an order, which is small in practice. We also determine conditions for the range of acceptance probabilities for the myopic policy to be optimal for small-sized orders. Using optimality conditions for a special case of the problem, we develop an adaptive variant of the myopic policy, and propose a new degree-based strategy that balances shipping costs and acceptance probabilities. Numerical testing suggests that the best-performing sequencing policy is within 1% of optimality on average. Moreover, using two years of data from a large omni-channel retailer in North America, we observe that adaptive policies, albeit more complex, are beneficial in reducing costs and split deliveries if acceptance rates can be estimated accurately. Second, we determine when the retailer should ship from stores or ration the inventory at the DC. We show that for single-item orders, the optimal policy has a threshold structure, where, remarkably, the highest priority region is also subject to rationing. We then consider the novel multi-unit-single-item rationing problem, and leverage the structure of the single-unit model to develop a heuristic. We numerically establish the efficacy of rationing models and our heuristic.

Leakage detection of an acoustic emission pipeline based on an improved transformer network

Pipeline leakage detection is an integral part of pipeline integrity management. Combining AE (Acoustic Emission) with deep learning is currently the most commonly used method for pipeline leakage detection. However, this approach is usually applicable only to specific situations and requires powerful signal analysis and computational capabilities. To address these issues, this paper proposes an improved Transformer network model for diagnosing faults associated with abnormal working conditions in acoustic emission pipelines. First, the method utilizes the temporal properties of the GRU and the positional coding of the Transformer to capture and feature extract the data point sequence position information to suppress redundant information, and introduces the largest pooling layer into the Transformer model to alleviate the overfitting phenomenon. Second, while retaining the original attention learning mechanism and identity path in the original DRSN, a new soft threshold function is introduced to replace the ReLU activation function with a new threshold function, and a new soft threshold module and adaptive slope module are designed to construct the improved residual shrinkage unit (ASB-STRSBU), which is used to adaptively set the optimal threshold. Finally, pipeline leakage is classified. The experimental results show that the NDRSN model is able to make full use of global and local information when considering leakage signals and can automatically learn and acquire the important parameters of the input features in the spatial and channel domains. By optimizing the GRU improved Transformer network recognition model, the method significantly reduces the model training time and computational resource consumption while maintaining high leakage recognition accuracy. The average accuracy reached 93.97%. This indicates that the method has good robustness in acoustic emission pipeline leakage detection.

Hydraulic Connectiveness Metric for the Analysis of Criticality in Water Distribution Networks

Capturing the criticality of a water distribution network (WDN) is difficult because of its many constituent factors. In terms of operation, the arrangement of demand nodes and how they connect have a significant influence. This study aims to integrate hydraulic and topologic aspects into a single criticality measure by adapting the structural hole influence matrix concept. This method applies the nodal demand to the corresponding pipes to construct a weighted network. The matrix stores each node’s local and global connection information, and the criticality value is then assigned based on the adjacency information. The criticality value can reveal the locations in terms of nodes or pipes that are vital for maintaining a network’s level of service. By analyzing pipe-failure scenarios, the criticality value can be related to the loss of performance. Assessing the nodal criticality change behavior under an increased stress scenario can help uncover the impacted areas. The metric for district metered area (DMA) creation demonstrates its potential as a weighting to be considered. This unified criticality metric enables the evaluation of nodes and pipes in a WDN, thereby enabling resilient and sustainable development planning.

Invariant point message passing for protein side chain packing.

Protein side chain packing (PSCP) is a fundamental problem in the field of protein engineering, as high-confidence and low-energy conformations of amino acid side chains are crucial for understanding (and designing) protein folding, protein-protein interactions, and protein-ligand interactions. Traditional PSCP methods (such as the Rosetta Packer) often rely on a library of discrete side chain conformations, or rotamers, and a forcefield to guide the structure to low-energy conformations. Recently, deep learning (DL) based methods (such as DLPacker, AttnPacker, and DiffPack) have demonstrated state-of-the-art predictions and speed in the PSCP task. Building off the success of geometric graph neural networks for protein modeling, we present the Protein Invariant Point Packer (PIPPack) which effectively processes local structural and sequence information to produce realistic, idealized side chain coordinates using -angle distribution predictions and geometry-aware invariant point message passing (IPMP). On a test set of ∼1400 high-quality protein chains, PIPPack is highly competitive with other state-of-the-art PSCP methods in rotamer recovery and per-residue RMSD but is significantly faster.

Banana Bunch Weight Estimation and Stalk Central Point Localization in Banana Orchards Based on RGB-D Images

Precise detection and localization are prerequisites for intelligent harvesting, while fruit size and weight estimation are key to intelligent orchard management. In commercial banana orchards, it is necessary to manage the growth and weight of banana bunches so that they can be harvested in time and prepared for transportation according to their different maturity levels. In this study, in order to reduce management costs and labor dependence, and obtain non-destructive weight estimation, we propose a method for localizing and estimating banana bunches using RGB-D images. First, the color image is detected through the YOLO-Banana neural network to obtain two-dimensional information about the banana bunches and stalks. Then, the three-dimensional coordinates of the central point of the banana stalk are calculated according to the depth information, and the banana bunch size is obtained based on the depth information of the central point. Finally, the effective pixel ratio of the banana bunch is presented, and the banana bunch weight estimation model is statistically analyzed. Thus, the weight estimation of the banana bunch is obtained through the bunch size and the effective pixel ratio. The R2 value between the estimated weight and the actual measured value is 0.8947, the RMSE is 1.4102 kg, and the average localization error of the central point of the banana stalk is 22.875 mm. The results show that the proposed method can provide bunch size and weight estimation for the intelligent management of banana orchards, along with localization information for banana-harvesting robots.

ADF‐Net: Attention‐guided deep feature decomposition network for infrared and visible image fusion

AbstractTo effectively enhance the ability to acquire information by making full use of the complementary features of infrared and visible images, the widely used image fusion algorithm is faced with challenges such as information loss and image blurring. In response to this issue, the authors propose a dual‐branch deep hierarchical fusion network (ADF‐Net) guided by an attention mechanism. Initially, the attention convolution module extracts the shallow features of the image. Subsequently, a dual‐branch deep decomposition feature extractor is introduced, where in the transformer encoder block (TEB) employs remote attention to process low‐frequency global features, while the CNN encoder block (CEB) extracts high‐frequency local information. Ultimately, the global fusion layer based on TEB and the local fusion layer based on CEB produce the fused image through the encoder. Multiple experiments demonstrate that ADF‐Net excels in various aspects by utilizing two‐stage training and an appropriate loss function for training and testing.

Research on bearing fault diagnosis based on novel MRSVD-CWT and improved CNN-LSTM

Abstract As a critical component in mechanical equipment, rolling bearings play a vital role in industrial production. Effective bearing fault diagnosis provides a more reliable guarantee for the safe operation of the industrial output. Traditional data-driven bearing fault diagnosis methods often have problems such as insufficient fault feature extraction and poor model generalization capabilities, resulting in reduced diagnostic accuracy. To solve these problems and significantly improve the diagnosis accuracy, this paper proposes a novel fault diagnosis method based on multi-resolution singular value decomposition (MRSVD), continuous wavelet transform (CWT), improved convolutional neural network (CNN) enhanced by convolutional block attention module (CBAM), and long short-term memory (LSTM). Through MRSVD, the vibration signal is decomposed layer by layer into multiple denoised signals, thus signal noise can be eliminated to the greatest extent to gain the optimal denoised signals; then through CWT, the optimal denoised signals are converted into two-dimensional time-frequency images so that the local and global characteristic information can be fully captured. Finally, through improved CNN-LSTM, feature extraction is greatly enhanced, resulting in high accuracy of fault diagnosis. Lots of experiments are organized to test the performance, and the experimental results show that the proposed method on various datasets has better diagnosis accuracy and generalization ability under different working conditions than other methods.

#1029 Factors associated with chronic kidney disease in patients with recurrent kidney stones: a case-control study

Abstract Background and Aims Kidney stones have been described in multiple studies as an independent risk factor for chronic kidney disease (CKD). However, this relationship remains partially understood, and there is limited local, regional, and national information available. While common factors contribute to both kidney stones and CKD development, other factors favoring CKD occurrence in patients with recurrent renal lithiasis have been reported. This study aimed to identify factors associated with chronic kidney disease in patients with recurrent renal lithiasis in the city of Cartagena, Colombia. Method This was an observational nested case-control study within a cohort, where cases were patients diagnosed with recurrent kidney stones who developed CKD during follow-up, and controls were patients with recurrent kidney stones without CKD during follow-up. The cohort comprised all adult patients with recurrent kidney stones between 2010 and 2019, without an initial diagnosis of CKD, and with a minimum follow-up period of two years or more. Data on demographic variables (age, gender, type of insurance), clinical factors (overweight, history of hyperuricemia, diabetes mellitus, hypertension, dyslipidemia, alcohol consumption, smoking, exercise habits), and variables related to recurrent kidney stones (course of kidney stones, onset time of CKD, metabolic disorders, history of renal colic, stone localization, number, and size) were collected. Variables associated with the development of chronic kidney disease were identified using association measures via the chi-square test. Variables of clinical and statistical significance were further analyzed for association using multivariable logistic regression. Results The study included 31 cases and 39 controls, with mean ages of 65.2±11.5 and 51.8±14.6 years, respectively (p=0.00002). The duration of recurrent kidney stones was 174.7±148.6 and 163.3±129.5 months for cases and controls (p=0.738). The onset time of CKD was 61.1±35.4 months. Among cases, 93.5% were men, 77.4% were hypertensive, and 67.9% had dyslipidemia, significantly differing from the controls (see Table 1). There were no differences between cases and controls concerning the duration of recurrent kidney stones, type of health insurance, overweight status, hyperuricemia, diabetes mellitus, exercise habits, metabolic study alterations, history of renal colic, renal stone localization, number, and size. The difference was borderline for alcohol consumption and smoking habits. Multivariable logistic regression, incorporating variables with statistical significance (age, gender, hypertension, alcohol consumption, smoking habit) into the model, revealed that higher age corresponds to a greater risk of CKD (Odds ratio 1.052; 95% CI, 1.003 to 1.103). Furthermore, the male gender exhibited a 5.5 times higher likelihood of suffering from CKD (Odds ratio 5.5; 95% CI, 1.2 to 26.5). Conclusion In patients with recurrent kidney stones, higher age, and male gender were identified as factors associated with the development of CKD.

A Mechanical Antenna for Long-Wave Communication by Integrating Piezoelectric Material and Magnets

Abstract The Chu's limit imposes a significant challenge for traditional antennas operating at extremely low frequencies, as they require a large size due to the long operating wavelength, thus limiting their applicability. To reduce the antenna size, this paper proposes a vibrating beam system for long-wave communication that leverages the inverse piezoelectric effect, vibration theory, and Maxwell's equations. A prototype utilizing beam structures is developed and examined experimentally. Furthermore, the frequency modulation of the vibrating beam system and signal transfer protocol are investigated in detail. The experimental results demonstrate that exciting different vibration modes of the vibrating beam system leads to varied electromagnetic signals in specific rules, enabling long-wave communications and ensuring confidentiality. This work offers valuable insights into the potential of local information exchange among close-range platforms. It also highlights the promising approach of integrating piezoelectric material and magnets within the vibrating beam system, showcasing their potential towards practical applications in long-wave communication.

An Ethnobotanical Study of Pinswari Community - a Preliminary Survey

The egregious, idyllic, non-xenophobic Pinswari community is an isolated community dwelling in the remote hills of the Tehri District of Uttar Pradesh, India. They are the descendants of the Jaads (Huniyas) and are still isolated in the sense that normally do not have marital' relation with the Garhwalis. Based on ethnobotanical survey (1983-1988) 108 plant species which are used as medicines, Ebre yielders, in arts and crafts, and in agricultural implements are reported. Correct botanical name, family, local name, collector's name (abbreviated as AKB) and number and detailed uses as given by local informants are given under each entry.

Comparison Utilization of UAV Images & Satellite Images for Geospatial Analysis

The rapid advancements in remote sensing technologies have opened new avenues for geospatial analysis by providing high-quality imagery and data. Unmanned Aerial Vehicles (UAVs) and satellites have emerged as prominent tools for capturing Earth's surface information, each offering unique advantages and limitations. This paper presents a comprehensive comparison of the utilisation of UAV images and satellite images in geospatial analysis. The practical implications of employing UAVs and satellites in diverse scenarios include environmental monitoring, disaster management, urban planning, and agriculture. The benefits of UAVs, such as their ability to capture high- resolution images with precision and on-demand deployment, are contrasted with satellites' global coverage and consistent monitoring capabilities. Integrating UAV and satellite data is emphasized as a complementary approach to geospatial analysis. Techniques for fusing data from these sources are discussed, enabling the creation of comprehensive datasets that combine detailed local information from UAVs with broader context from satellite imagery.

Fault diagnosis for blast furnace ironmaking process based on randomized local fisher discriminant analysis

AbstractFault diagnosis plays a vital role in ensuring the operation safety of blast furnaces and improving the quality of molten iron in the ironmaking and steelmaking industry. The blast furnace ironmaking process (BFIP) is intrinsically nonlinear. To address the nonlinearity issue of BFIP, a novel fault diagnosis approach that combines the randomized method, local structure information, and Fisher discriminant analysis is proposed in this paper. Using a randomized feature map, the process data is first mapped onto a randomized explicit low‐dimensional feature space. Compared to kernel methods, explicit low‐dimensional random Fourier features considerably reduce the computational cost, particularly for real‐time fault diagnosis for a large training dataset or a large‐scale process. Additionally, the local structure information contained in the randomized low‐dimensional feature space is extracted. The fault diagnosis performance is improved through the exploration of the local structure of random Fourier features. Finally, the blast furnace iron‐marking process state is determined using Bayesian inference. Case studies on a real‐world BFIP are carried out to demonstrate the superior performance of the proposed method in comparison with other related methods.