Distance Information Research Articles

Wireless UV collaborative RSSI and the AOA hybrid localization method for UAV swarms

Wireless ultraviolet (UV) light can guarantee the inter-copter positioning accuracy of cluster-flying unmanned aerial vehicles (UAVs) in an electromagnetic confrontation environment. By combining wireless UV received signal strength indication (RSSI) localization and the angle of arrival (AOA) localization algorithm, a UV-hybrid localization method is proposed for UV communication collaboration between UAV swarms. The method collects the UV signal strength between the anchor UAV node and the unknown UAV node to obtain the inter-aircraft distance information, establishes a Gaussian hybrid noise model based on semi-definite relaxation, and uses the UV angle of the arrival estimation to solve the angle between the UAVs to achieve the maximum likelihood estimation of node positions in UAV swarms. A simulation comparison of the UV-hybrid localization algorithm, weighted least squares, and the Gaussian hybrid semi-definite planning localization algorithm is carried out. The results show that the performance of the UV-hybrid localization algorithm is close to the Cramer–Rao lower bound, and the average localization error is reduced by 32.9% and 15.6% compared to weighted least squares and Gaussian hybrid semi-definite planning algorithms; the algorithm of this paper achieves the node localization with fewer iterations, and it has a higher accuracy and efficiency of localization than the other algorithms.

Chicken body temperature monitoring method in complex environment based on multi-source image fusion and deep learning

Severe diseases in chickens present substantial risks to poultry husbandry industry. Notably, alterations in body temperature serve as critical clinical indicators of these diseases. Consequently, timely and accurate monitoring of body temperature is essential for the early detection of severe health issues in chickens. This study presents a novel method for simultaneous body temperature detection of multiple chickens in caged poultry environments. A dataset of 2896 chicken head images was developed. The YOLOv8n-mvc model was created to accurately detect chicken head positions and extracted temperature data and distance information through the fusion of RGB, thermal infrared, and depth images. The chicken head temperature was calibrated using distance information. The YOLOv8n-mvc model established in this study achieved a precision of 91.6 %, recall of 92.5 %, F1 score of 92.0 %, and mAP@0.5 of 96.0 %. The model was successfully deployed on an edge computing device for validation tests, demonstrating its feasibility for chicken body temperature detection. This study provides a reference for developing a chicken health monitoring system based on body temperature.

Multi-Target Tracking with Multiple Unmanned Aerial Vehicles Based on Information Fusion

In high-altitude scenarios, targets tend to occupy a small number of pixels within the UAV’s field of view, resulting in substantial errors when identity recognition is attempted based solely on appearance features during multi-UAV joint tracking. Existing methodologies typically propose projecting multi-view data onto a single plane and leveraging distance information for identity association; however, their accuracy remains low as they are contingent on one-dimensional target information. To address this limitation, this paper introduces the UAVST-HM (UAV Swarm Tracking in High-altitude scenarios for Multiple targets) model, specifically designed to handle the characteristics of targets in the field of view of multiple UAVs at high altitudes. Initially, we develop techniques for extracting targets’ appearance, geometric, and distribution features. Subsequently, adaptive weights, calculated based on the mean of the respective features, are devised to amalgamate these diverse features, thereby constructing a cost matrix for cross-view target identity matching. This matrix is processed through the Hungarian algorithm, and multi-view target identity association is ultimately achieved via threshold filtering. On the MDMT dataset, our method enhances the MDA indicator, which assesses cross-view target identity matching, by 1.78 percentage points compared to the current state of the art. This significant enhancement substantially improves the overall efficacy of multi-UAV joint visual tracking from a high-altitude perspective.

ЗМІСТОВЕ НАПОВНЕННЯ ПОНЯТТЯ «ІНФОРМАЦІЙНО-ОСВІТНЄ СЕРЕДОВИЩЕ УНІВЕРСИТЕТУ»

Based on the above definitions, it is proved that today there is no terminological unity of scientists in the interpretation of the content of the concept of «information and educational environment»: scientists interpret the information and educational environment as a set of hardware and software tools for storing, processing and transmitting information; as a set of conditions for the implementation of ICT in the educational process, as a pedagogical system (or a subsystem of the pedagogical system), etc. Some definitions focus on the educational component, others on the technical and teaching tools used to implement the educational process, while others focus on software, etc. It has been found that the information and educational environment of a university should be understood as a purposefully built innovative pedagogical system in educational activities, created on the basis of modern pedagogical, information and communication and distance technologies, methods and integration of computer-oriented tools with information and resource support, designed to adapt the modern educational process to the conditions of the information society. IES is a complex system consisting of various subsystems (pedagogical, informational, technical and educational-methodical), which provides educational and related processes, as well as participants in these processes (students, teachers), a set of conditions, technologies, methods, tools, devices, resources. The structure of IES (level of IES hardware; level of software and information systems; level of communication technologies; level of education technologies; organizational/functional level (forms of conducting educational and extracurricular activities, management of the educational process); resource level (accumulation, storage and provision of resources) is outlined, the principles of building the information and educational environment of the university (multicomponent, integrity, complexity and information content, distribution, adaptability, unity, fundamentality, security).

Conditional entropy-based hybrid DDoS detection model for IoT networks

In a distributed denial-of-service (DDoS) attack, an attacker channelizes the resources of a botnet to launch denial of service attack on the victim. The increased use of IoT devices and dependence of users on e-services like online shopping and online payments have elevated the liability risks. The entropy provides a significant measure of randomness. The variation in entropy of traffic features determines the presence of abrupt traffic. This paper uses entropy and conditional entropy to achieve insights on data and feeds it to the proposed 2-stage detection approach for multi-class classification. The proposed model employs four classifiers for first hand classification. Further, stacking generalization-based second stage achieves the final detection process. The recently launched CIC IoT 2023 dataset is used to illustrate the findings of the study. The proposed approach produces an accuracy of 99.86%. Further, this paper utilizes relative entropy for the determination of deflection of traffic behavior between the attack and legitimate samples. Comparisons have been made among symmetric versions of information divergence, ϕ-divergence and Kullback–Leibler divergence along with, Hellinger distance and total variation distance. It is found that the information distance gives a better differentiation between the entropy of legitimate traffic and attack traffic. Significance Statement Entropy has been manipulated to define the nature of incoming traffic for any rule-based detection. This work explores the significance of conditional entropy for the ML-based detection of DDoS attacks in a recently launched IoT-based dataset. Additionally, the effectiveness of KL-divergence, information divergence, ϕ-divergence, Hellinger distance and total variation distance is compared for differentiating between legitimate traffic and attack traffic.

A Probabilistic Approach in the Search Space of the Molecular Distance Geometry Problem.

The discovery of the three-dimensional shape of protein molecules using interatomic distance information from nuclear magnetic resonance (NMR) can be modeled as a discretizable molecular distance geometry problem (DMDGP). Due to its combinatorial characteristics, the problem is conventionally solved in the literature as a depth-first search in a binary tree. In this work, we introduce a new search strategy, which we call frequency-based search (FBS), that for the first time utilizes geometric information contained in the protein data bank (PDB). We encode the geometric configurations of 14,382 molecules derived from NMR experiments present in the PDB into binary strings. The obtained results show that the sample space of the binary strings extracted from the PDB does not follow a uniform distribution. Furthermore, we compare the runtime of the symmetry-based build-Up (SBBU) algorithm (the most efficient method in the literature to solve the DMDGP) combined with FBS and the depth-first search (DFS) in finding a solution, ascertaining that FBS performs better in about 70% of the cases.

Differential Positioning with Bluetooth Low Energy (BLE) Beacons for UAS Indoor Operations: Analysis and Results.

Localization of unmanned aircraft systems (UASs) in indoor scenarios and GNSS-denied environments is a difficult problem, particularly in dynamic scenarios where traditional on-board equipment (such as LiDAR, radar, sonar, camera) may fail. In the framework of autonomous UAS missions, precise feedback on real-time aircraft position is very important, and several technologies alternative to GNSS-based approaches for UAS positioning in indoor navigation have been recently explored. In this paper, we propose a low-cost IPS for UAVs, based on Bluetooth low energy (BLE) beacons, which exploits the RSSI (received signal strength indicator) for distance estimation and positioning. Distance information from measured RSSI values can be degraded by multipath, reflection, and fading that cause unpredictable variability of the RSSI and may lead to poor-quality measurements. To enhance the accuracy of the position estimation, this work applies a differential distance correction (DDC) technique, similar to differential GNSS (DGNSS) and real-time kinematic (RTK) positioning. The method uses differential information from a reference station positioned at known coordinates to correct the position of the rover station. A mathematical model was established to analyze the relation between the RSSI and the distance from Bluetooth devices (Eddystone BLE beacons) placed in the indoor operation field. The master reference station was a Raspberry Pi 4 model B, and the rover (unknown target) was an Arduino Nano 33 BLE microcontroller, which was mounted on-board a UAV. Position estimation was achieved by trilateration, and the extended Kalman filter (EKF) was applied, considering the nonlinear propriety of beacon signals to correct data from noise, drift, and bias errors. Experimental results and system performance analysis show the feasibility of this methodology, as well as the reduction of position uncertainty obtained by the DCC technique.

Electrotactile proprioception training improves finger control accuracy and potential mechanism is proprioceptive recalibration

This study presents a novel training technique, visual + electrotactile proprioception training (visual + EP training), which provides additional proprioceptive information via tactile channel during motor training to enhance the training effectiveness. In this study, electrotactile proprioception delivers finger aperture distance information in real-time, by mapping frequency of electrical stimulation to finger aperture distance. To test the effect of visual + EP training, twenty-four healthy subjects participated in the experiment of matching finger aperture distance with distance displayed on screen. Subjects were divided to three groups: the first group received visual training and the other two groups received visual + EP training with or without a post-training test with electrotactile proprioception. Finger aperture control error was measured before and after the training (baseline, 15-min post, 24-h post). Experimental data suggest that both training methods decreased finger aperture control error at 15-min post-training. However, at 24-h post-training, the training effect was fully retained only for the subjects who received visual + EP training, while it washed out for the subjects with visual training. Distribution analyses based on Bayesian inference suggest that the most likely mechanism of this long-term retention is proprioceptive recalibration. Such applications of artificially administered sense have the potential to improve motor control accuracy in a variety of applications.

Environmental supervision distance, government-enterprise collusion and corporate environmental information disclosure

Based on the framework of government-enterprise collusion and proactive government regulation, this study examines the impact of the Environmental Protection Agency (EPA) regulatory distance on corporate environmental information disclosure (EID) using data from listed companies in China from 2009 to 2021. The results of the study show that: (1) The closer the geographical distance between the local EPA and the company, the poorer the quality, which verifies the collusion effect. Our results are robust after a series of robustness checks. (2) The collusion effect of geographical distance primarily works by enhancing the ability of companies to seek environmental protection. (3) The impact is greater when the sample company belongs to non-sub-provincial municipal, pollution industry, state-owned, large-scale, and mature enterprises. (4) The central environmental inspections and media attention can alleviate the collusion effect of environmental regulatory distance. This study provides new empirical evidence for the " distance decay effect " in geo-economics and offers new insights from the perspective of geo-economics for measuring government-enterprise collusion.

Encryption Techniques in Near Field Communication (NFC): Challenges, Applications, and Future Directions

With the growth of the need of information transmission and communication, Near Field Communication(NFC) technique is widely used in different areas for its convenience and simplicity,especially those areas that require both high efficiency and low cost of information transmission. However, the security of NFC itself is not ensured due to the risk of information leakage caused by technical features of short distance information transmission. Information encryption techniques are able to help NFC to be more reliable and safer from the danger of data leakage. There are several kinds of encryption techniques for NFC encryption.Due to the impact of multiple different techniques on the universality of NFC, placing different encryption techniques under the same standard can be an effective research method to improve collaboration efficiency of different encryption techniques. This paper mainly introduces common encryption techniques for NFC,including their features and applicable scenarios, and attempt to conduct technical analysis on existing encryption technologies for finding possible improving directions.

Efficient Semantic Segmentation for Large-Scale Agricultural Nursery Managements via Point Cloud-Based Neural Network

Remote sensing technology has found extensive application in agriculture, providing critical data for analysis. The advancement of semantic segmentation models significantly enhances the utilization of point cloud data, offering innovative technical support for modern horticulture in nursery environments, particularly in the area of plant cultivation. Semantic segmentation results aid in obtaining tree components, like canopies and trunks, and detailed data on tree growth environments. However, obtaining precise semantic segmentation results from large-scale areas can be challenging due to the vast number of points involved. Therefore, this paper introduces an improved model aimed at achieving superior performance for large-scale points. The model incorporates direction angles between points to improve local feature extraction and ensure rotational invariance. It also uses geometric and relative distance information for better adjustment of different neighboring point features. An external attention module extracts global spatial features, and an upsampling feature adjustment strategy integrates features from the encoder and decoder. A specialized dataset was created from real nursery environments for experiments. Results show that the improved model surpasses several point-based models, achieving a Mean Intersection over Union (mIoU) of 87.18%. This enhances the precision of nursery environment analysis and supports the advancement of autonomous nursery managements.

Remote sensing and feminist critique: Reappropriations of sensing across distance

Remote sensing technologies and Geographic Information Systems (GIS) have sparked diverse conceptual and critical responses, with feminist approaches in particular undergoing notable transformations. This article traces and contextualizes these changes, emphasizing the re-evaluation of the concept of “distance” in feminist critiques of remote sensing (RS) and GIS from the 1990s onwards. It highlights a shift from outright rejection of GIS/RS technologies as tools of masculinist, positivistic science to a redefinition of remotely sensed information and imbricated understandings of distance and proximity. The article argues that researchers view issues of masculinism from markedly different perspectives and re-evaluate the role of technology-mediated seeing and imagining, resulting in a re-evaluation of physical distance in its relationship to infrastructure, access, and care. What has transformed is what it means to be “critical” toward as well as the possibility to be “critical” with remote sensing from a feminist point of view.

Word vector embedding and self-supplementing network for Generalized Few-shot Semantic Segmentation

Under the condition of sufficient base class samples and a few novel class samples, Generalized Few-shot Semantic Segmentation (GFSS) classifies each pixel in the query image as base class, novel class, or background. A standard GFSS approach involves two training stages: base class learning and novel class updating. However, inter-class interference and information loss which contribute to the poor performance of GFSS, have not been synthetical considered. To address the problem, we propose an Embedded-Self-Supplementing Network (ESSNet), i.e., semantic word embedding and query set self-supplementing information to enhance segmentation accuracy. Specifically, the semantic word embedding module employs distance information between word vectors to assist the model in learning the distance between class prototypes. In order to transform the semantic word vector prototypes from the semantic space to the visual embedding space, we designed a triplet loss function to supervise the word vector embedding module, where the word vector prototype serves as an anchor and positive-negative samples are collected among the general features of the support image. To compensate for the information loss caused by using prototypes to represent classes, we propose a self-supplementing module to mine the information contained in the query image. Specifically, this module first makes a preliminary prediction on the query image, then selects high-confidence area to form pseudo labels, and finally uses pseudo labels to extract query prototypes to supplement the missing information. Extensive experiments on PASCAL-5i and COCO-20i show that ESSNet has superior performance and outperforms state-of-the-art methods in all settings.

Optimal Layout of Urban Green Space System Based on the Coupling Model of Spatial Reconstruction

In the layout of urban planning, the rational design of green land includes the optimal choice of landscape and vegetation, which needs to solve the conflict of local/global optimal solution and understand the rationality of urban green layout with multi-objective natural language. According to the demand for multi-objective optimal solutions in urban green land planning, we study the target deep learning algorithm based on space vector mutation operation. In this paper, we propose to search for a local optimal solution based on Diversity Indicator Double and Niched Local Search Evolutionary Algorithm diversity fitness and filter the information of objective space and decision space through the niche method. It intends to avoid the imbalance between the diversity of goal space and decision space caused by the wrong distance information. By improving genetic diversity, we take the complex changes of gene vector reaction in generating offspring as the research focus. Based on the improvement of multi-objective non-dominated genetic variation, we search for the optimal transfer function relationship in the primary stage of variation to achieve the goal. For high-dimensional planning with constraints, we first select the coupling model to reduce the dimension, and then combine the spatial reconstruction data clustering to complete the clustering. Priority is given to the optimal selection of the output of the non-dominated solution set obtained in each stage to improve the algorithm’s performance. Experiments show that the proposed algorithm, combined with the use of different mutation strategies, outperforms the current mainstream algorithms in solving multi-objective problems of natural language understanding, especially in solving complex relational problems, which is 10.2% and 15.6% higher than other algorithms. The frequency of parameter operation can be updated in real-time, which strengthens the convergence effect of the algorithm. It is proved that the research content of this paper has better application potential in multi-objective spatial decision-making. The research content of this paper can not only achieve intelligent layout optimization for urban green space system but also carry out reasoning for large-scale spatial layout. The effect is higher than the current mainstream algorithm and plays a role in promoting science and technology for future urban construction and long-term planning.

Information transfer from spatial to social distance in rats: implications for the role of the posterior parietal cortex in spatial-social integration.

Humans and other social animals can represent and navigate complex networks of social relationships in ways that are suggestive of representation and navigation in space. There is some evidence that cortical regions initially required for processing space have been adapted to include processing of social information. One candidate region for supporting both spatial and social information processing is the posterior parietal cortex (PPC). We examined the hypothesis that rats can transfer or generalize distance information across spatial and social domains and that this phenomenon requires the PPC. In a novel apparatus, rats learned to discriminate two conspecifics positioned at different spatial distances (near vs. far) in a goal-driven paradigm. Following spatial learning, subjects were tested on probe trials in which spatial distance was replaced with social distance (cagemate vs. less familiar conspecific). The PPC was chemogenetically inactivated during a subset of probe sessions. We predicted that, in control probe trials, subjects would select conspecifics whose social distance matched the previously learned spatial distance. That is, if trained on the near distance, the rat would choose the highly familiar cagemate, and if trained on the far distance, the rat would choose the less familiar conspecific. Subjects learned to discriminate conspecifics based on spatial distance in our goal-driven paradigm. Moreover, choice for the appropriate social distance in the first probe session was significantly higher than chance. This result suggests that rats transferred learned spatial information to social contexts. Contrary to our predictions, PPC inactivation did not impair spatial to social information transfer. Possible reasons are discussed. To our knowledge, this is the first study to provide evidence that spatial and social distance are processed by shared cognitive mechanisms in the rat model.

MisORFPred: A Novel Method to Mine Translatable sORFs in Plant Pri-miRNAs Using Enhanced Scalable k-mer and Dynamic Ensemble Voting Strategy.

The primary microRNAs (pri-miRNAs) have been observed to contain translatable small open reading frames (sORFs) that can encode peptides as an independent element. Relevant studies have proven that those of sORFs are of significance in regulating the expression of biological traits. The existing methods for predicting the coding potential of sORFs frequently overlook this data or categorize them as negative samples, impeding the identification of additional translatable sORFs in pri-miRNAs. In light of this, a novel method named misORFPred has been proposed. Specifically, an enhanced scalable k-mer (ESKmer) that simultaneously integrates the composition information within a sequence and distance information between sequences is designed to extract the nucleotide sequence features. After feature selection, the optimal features and several machine learning classifiers are combined to construct the ensemble model, where a newly devised dynamic ensemble voting strategy (DEVS) is proposed to dynamically adjust the weights of base classifiers and adaptively select the optimal base classifiers for each unlabeled sample. Cross-validation results suggest that ESKmer and DEVS are essential for this classification task and could boost model performance. Independent testing results indicate that misORFPred outperforms the state-of-the-art methods. Furthermore, we execute misORFPerd on the genomes of various plant species and perform a thorough analysis of the predicted outcomes. Taken together, misORFPred is a powerful tool for identifying the translatable sORFs in plant pri-miRNAs and can provide highly trusted candidates for subsequent biological experiments.

Should voice navigation prompts be earlier and more complicated? A study of drivers’ visual attention, physiology and operating performance

Voice navigation systems can help drivers access information about road conditions and make correct decisions, but inappropriate information level and information prompt timing may lead to untimely processing of information, distraction, or even misjudgment of drivers, which in turn may cause safety hazards. The purpose of this study is to investigate the effects of voice information level and information prompt timing on drivers’ driving behavior and physiological characteristics. Twenty-seven participants were recruited to conduct driving simulator experiments. The results showed that the driver’s visual attention is more focused and the driving performance is better when the voice navigation prompts contain four information units (driving directions, traffic facility information, distance information, and road condition information at the same time). Moreover, when the voice navigation prompts with four information units were played 300 meters from the intersection, the driver’s overall driving performance was the highest. The results offer additional theoretical insights for the design of voice navigation systems, aiding drivers in accurately understanding driving conditions and providing them with ample reaction time to enhance their driving decisions.

Distance preserving machine learning for uncertainty aware accelerator capacitance predictions

Abstract Accurate uncertainty estimations are essential for producing reliable machine learning models, especially in safety-critical applications such as accelerator systems. Gaussian process models are generally regarded as the gold standard for this task; however, they can struggle with large, high-dimensional datasets. Combining deep neural networks with Gaussian process approximation techniques has shown promising results, but dimensionality reduction through standard deep neural network layers is not guaranteed to maintain the distance information necessary for Gaussian process models. We build on previous work by comparing the use of the singular value decomposition against a spectral-normalized dense layer as a feature extractor for a deep neural Gaussian process approximation model and apply it to a capacitance prediction problem for the High Voltage Converter Modulators in the Oak Ridge Spallation Neutron Source. Our model shows improved distance preservation and predicts in-distribution capacitance values with less than 1% error.

Transcriptomic Signature of Spatial Navigation in Brains ofDesert Ants.

Navigation is crucial for central-place foragers to locate food and return to the nest. Cataglyphis ants are renowned for their advanced navigation abilities, relying on landmark cues and path integration. This study aims to uncover the transcriptomic basis of exceptional spatial learning in the central nervous system of Cataglyphis niger. Ants navigated a maze with a food reward, and we examined expression changes linked to correct decisions in subsequent runs. Correct decisions correlated with expression changes in the optic lobes, but not the central brain, showing a downregulation of genes associated with sucrose response and Creb3l1. The latter gene is homologous to Drosophila crebA, which is essential for long-term memory formation. To understand how ants use distance information during path integration, we analyzed expression shifts associated with the last distance traveled. We uncovered a transcriptomic footprint in the central brain, but not in the optic lobes, with genes enriched for energy consumption and neurological functions, including neuronal projection development, synaptic target inhibition, and recognition processes. This suggests that transcriptional activity in the central brain is necessary for estimating distance traveled, which is crucial for path integration. Our study supports the distinct roles of different brain parts for navigation in Cataglyphis ants.

Acpro: fusion of embedded ambient light sensor and image sensor for visible light positioning

Abstract With the increasing demand for indoor positioning, visible light positioning (VLP) systems have become an optics technologies research hotspot due to their high accuracy and avoidance of electromagnetic interference. However, current photodiode (PD)-based and image sensor (IS)-based VLP systems still suffer from high costs at the receiver, and the problem of the trade-off between field of view (FoV) and positioning accuracy. Therefore, we propose a smartphone-based low-cost, and high-precision VLP system, Acpro. The system combines the smartphone’s embedded ambient light sensor and IS as the receiver for the positioning. First, for the low sampling rate of the ambient light sensor, we use the frequency aliasing characteristic to identify the LED’s beacon through the dynamic undersampling principle. Then, to increase the FoV and positioning accuracy, we use the standard distance information corrected by the IS to improve the noise distance calculated by the ambient light sensor. Further, the corrected distance information is used for positioning. Extensive results show that our system can achieve sub-meter positioning accuracy.