Visual Line Research Articles

Pixel-wise navigation line extraction of cross-growth-stage seedlings in complex sugarcane fields and extension to corn and rice

Extracting the navigation line of crop seedlings is significant for achieving autonomous visual navigation of smart agricultural machinery. Nevertheless, in field management of crop seedlings, numerous available studies involving navigation line extraction mainly focused on specific growth stages of specific crop seedlings so far, lacking a generalizable algorithm for addressing challenges under complex cross-growth-stage seedling conditions. In response to such challenges, we proposed a generalizable navigation line extraction algorithm using classical image processing technologies. First, image preprocessing is performed to enhance the image quality and extract distinct crop regions. Redundant pixels can be eliminated by opening operation and eight-connected component filtering. Then, optimal region detection is applied to identify the fitting area. The optimal pixels of plantation rows are selected by cluster-centerline distance comparison and sigmoid thresholding. Ultimately, the navigation line is extracted by linear fitting, representing the autonomous vehicle’s optimal path. An assessment was conducted on a sugarcane dataset. Meanwhile, the generalization capacity of the proposed algorithm has been further verified on corn and rice datasets. Experimental results showed that for seedlings at different growth stages and diverse field environments, the mean error angle (MEA) ranges from 0.844° to 2.96°, the root mean square error (RMSE) ranges from 1.249° to 4.65°, and the mean relative error (MRE) ranges from 1.008% to 3.47%. The proposed algorithm exhibits high accuracy, robustness, and generalization. This study breaks through the shortcomings of traditional visual navigation line extraction, offering a theoretical foundation for classical image-processing-based visual navigation.

Performance on the Concussion Balance Test Is Indicative of Time to Recovery in Athletes Following Sports-Related Concussion: An Exploratory Analysis.

Sports-related concussions (SRCs) are commonly occurring injuries among athletic and recreationally active populations. SRCs can result in vestibular dysfunction that should resolve before returning to activity. It has been suggested that vestibular impairment is a factor that may influence recovery time. The objective of this study was to evaluate the effect of vestibular function on recovery following SRC. Retrospective chart review. Multidisciplinary Concussion Clinic. A total of 32 patient charts (21 males, 15.34 [1.47]y, 171.29 [8.44]cm, 68.37 [15.47]kg) from patients diagnosed with SRC presenting to a concussion clinic between August of 2016 and July 2017 with clinician-identified vestibular involvement were included. Scores on the Post-Concussion Symptom Scale, Dynamic Visual Acuity Test, Gaze Stabilization Test, Concussion Balance Test (COBALT), and other clinical data were used for analysis. Descriptive statistics were calculated for all variables. Pearson correlations were used to identify variables related to time to recovery. Variables were entered into a forward linear regression model. Moderate to good relationships were identified between days to recovery and Dynamic Visual Acuity Test lines lost in the leftward direction (1.17 [0.52]; r = .39, P = .04), COBALT-condition 8 sway velocity (1.20 [0.18]; r = .44, P = .01), and days to successful completion of the COBALT (14.72 [8.35]; r = .63, P < .001). Patients' predicted time to recover was equal to 14.61 (0.86) (days to successful COBALT). The model was significant (P < .001, R2 = .30). The single predictor of time to recover was the number of days to successful completion of the COBALT. Thus, the ability to complete the task may be more informative than the performance on the task in predicting recovery time.

A Multimodal Perception System for Precise Landing of UAVs in Offshore Environments

ABSTRACTThe integration of precise landing capabilities into unmanned aerial vehicles (UAVs) is crucial for enabling autonomous operations, particularly in challenging environments such as the offshore scenarios. This work proposes a heterogeneous perception system that incorporates a multimodal fiducial marker, designed to improve the accuracy and robustness of autonomous landing of UAVs in both daytime and nighttime operations. This work presents ViTAL‐TAPE, a visual transformer‐based model, that enhance the detection reliability of the landing target and overcomes the changes in the illumination conditions and viewpoint positions, where traditional methods fail. VITAL‐TAPE is an end‐to‐end model that combines multimodal perceptual information, including photometric and radiometric data, to detect landing targets defined by a fiducial marker with 6 degrees‐of‐freedom. Extensive experiments have proved the ability of VITAL‐TAPE to detect fiducial markers with an error of 0.01 m. Moreover, experiments using the RAVEN UAV, designed to endure the challenging weather conditions of offshore scenarios, demonstrated that the autonomous landing technology proposed in this work achieved an accuracy up to 0.1 m. This research also presents the first successful autonomous operation of a UAV in a commercial offshore wind farm with floating foundations installed in the Atlantic Ocean. These experiments showcased the system's accuracy, resilience and robustness, resulting in a precise landing technology that extends mission capabilities of UAVs, enabling autonomous and Beyond Visual Line of Sight offshore operations.

Exploratory Pilot Study Engages Community Health Workers to Test Drone-Based Package Delivery System for Personal Protective Equipment in High-Risk Appalachia Population.

During the COVID-19 pandemic, high-risk clients' and caregivers' access to essential personal protective equipment (PPE) was limited especially in many remote areas of Appalachia. A multidisciplinary team of community and university partners explored how to coordinate the use of community health workers (CHWs) and drone technologies to increase access to PPE in rural and remote Appalachian regions. CHWs recruited 10 Homeplace clients in an exploratory study of drone-based package delivery of PPE to assess importance and effectiveness of PPE self-efficacy related to PPE use, use of PPE, and ease and acceptability of drone delivery (following delivery only). CHWs educated each participant via in-person and Zoom meetings on the reasons for using PPE and proper use of PPE using Centers for Disease Control and Prevention guidelines. Most participants found the drone delivery of PPE easy and 80% were extremely satisfied with the drone delivery process. The frequency of mask-wearing increased from 60% at baseline to 90% at follow-up. On average, participants rated all types of PPE as effective in preventing the spread of disease. Drone officials used the findings of this pilot study to develop a waiver application to the U.S. Federal Aviation Administration to request permission to fly beyond visual line of sight in remote areas. Aerial drone technology could be a cutting-edge approach to health promotion in remote areas. The study results provide the proof of concept to assist investigators in designing future projects to promote healthy homes by collecting air and water samples and testing novel interventions deploying drone technology in remote Appalachian regions.

Drones in Precision Agriculture: A Comprehensive Review of Applications, Technologies, and Challenges

In the face of growing challenges in modern agriculture, such as climate change, sustainable resource management, and food security, drones are emerging as essential tools for transforming precision agriculture. This systematic review, based on an in-depth analysis of recent scientific literature (2020–2024), provides a comprehensive synthesis of current drone applications in the agricultural sector, primarily focusing on studies from this period while including a few notable exceptions of particular interest. Our study examines in detail the technological advancements in drone systems, including innovative aerial platforms, cutting-edge multispectral and hyperspectral sensors, and advanced navigation and communication systems. We analyze diagnostic applications, such as crop monitoring and multispectral mapping, as well as interventional applications like precision spraying and drone-assisted seeding. The integration of artificial intelligence and IoTs in analyzing drone-collected data is highlighted, demonstrating significant improvements in early disease detection, yield estimation, and irrigation management. Specific case studies illustrate the effectiveness of drones in various crops, from viticulture to cereal cultivation. Despite these advancements, we identify several obstacles to widespread drone adoption, including regulatory, technological, and socio-economic challenges. This study particularly emphasizes the need to harmonize regulations on beyond visual line of sight (BVLOS) flights and improve economic accessibility for small-scale farmers. This review also identifies key opportunities for future research, including the use of drone swarms, improved energy autonomy, and the development of more sophisticated decision-support systems integrating drone data. In conclusion, we underscore the transformative potential of drones as a key technology for more sustainable, productive, and resilient agriculture in the face of global challenges in the 21st century, while highlighting the need for an integrated approach combining technological innovation, adapted policies, and farmer training.

Large-Area UAS-Based Forest Health Monitoring Utilizing a Hydrogen-Powered Airship and Multispectral Imaging

Abstract. Climate change is threatening forest ecosystems worldwide by inducing various abiotic and biotic disturbances. In Europe, the European spruce bark beetle (Ips typographus L.) poses a significant threat, causing serious mortality in mature Norway spruce (Picea abies H. Karst.) stands. Rapidly evolving remote sensing technologies offer valuable tools for monitoring forest health, enabling timely management operations. This study presents a novel approach for large-area forest health monitoring using Uncrewed Aircraft Systems (UAS) and multispectral imaging. The research focuses on a hydrogen-powered Beyond Visual Line of Sight (BVLOS) airship for efficient monitoring of disturbances caused by I. typographus. A specific challenge is training machine learning models capable of covering wide areas. Our objective was to study the potential of deep learning models, including transfer learning and fine-tuning techniques, in developing the scalability and accuracy of UAS-based monitoring for detecting individual spruce trees and classifying their health. The approach was empirically evaluated in a study site in North Karelia, Finland. A multispectral image dataset was collected over a 1.3 km2 test area in May 2023 in a BVLOS setting operated from a command centre 75 km away. The results indicated that employing transfer learning significantly improved classification accuracy compared to training models from scratch, showing potential for implementing scalable machine learning methods for large-area UAS surveys. The best model yielded F1-scores of 0.936 for healthy, 0.955 for dead, and 0.817 for non-spruce classes. Furthermore, the results indicated that BVLOS airships offered high accuracy while reducing emissions and labour associated with UAS monitoring.

Femtosecond Laser-Assisted Graft Preparation and Implantation of Corneal Allogeneic Intrastromal Ring Segments for Corneal Ectasia: 1-Year Results.

To evaluate the outcomes of patients treated with corneal allogeneic intrastromal ring segments cut with femtosecond laser (Femto-CAIRS) without concomitant corneal crosslinking. Patients with keratoconus treated with Femto-CAIRS at the American University of Beirut Medical Center were included (May 2022-January 2023). A proprietary software program was developed on the femtosecond laser to cut allogeneic segments. Visual, refractive, tomographic, aberrometric, and epithelial data by anterior segment optical coherence tomography were measured at baseline and 1, 3, 6, and 12 months postoperatively. 20 eyes of 15 patients were included and followed up for 12 months. The manifest refraction spherical equivalent and cylinder improved from -6.79 ± 4.9 diopter (D) and -4.25 ± 1.8 D to -1.88 ± 2.9 D (P < 0.001) and -2.64 ± 1.4 D (P = 0.01) 12 months postoperatively, respectively. 75% of eyes gained 3 or more corrected distance visual acuity lines, most of which (65%) gained 4 lines or more 12 months postoperatively. The maximum keratometry and vertical coma decreased by 5.2 D (P < 0.001) and 1 μm (P = 0.001), respectively, 3 months postoperatively and remained stable until 12 months. The largest anterior stromal elevation over the central 5-mm diameter decreased from 36.0 ± 18.2 μm preoperatively to 19.9 ± 9.25 μm at 1 week postoperatively (P < 0.001) and remained relatively stable. Epithelial thickness over the cone increased relative to baseline starting 1 month postoperatively and becoming stable after 6 months while the mean epithelial thickness central to the ring peaked at 1 month after which it decreased to reach a plateau at 6 months. The Femto-CAIRS procedure improves visual and tomographic parameters and allows repeatable and safe results with the possibility of customization for individualized management.

Tightly coupled stereo vision-inertial odometry based on point and line feature

Abstract To improve the accuracy and robustness of visual simultaneous localization and mapping (SLAM) in low-texture environments, this paper proposes a robust and fast stereo vision inertial SLAM pose estimation method that combines point and line features with an inertial measurement unit (IMU). The method tightly couples visual point and line features with IMU constraints, forming a least-squares problem through the error of each constraint term for nonlinear optimization. To address the issues of over-segmentation and time consumption in traditional line segment detection (LSD) algorithms, an improved LSD algorithm is adopted to accelerate line feature extraction. This approach merges nearby line segments based on spatial geometric relationships and filters out invalid segments, improving the time efficiency of the algorithm. Finally, experiments conducted in low-texture environments demonstrate that our algorithm achieves high localization accuracy and robustness.

“I Don’t Want War in My House”: Young Children’s Meaning‐Making of War and Peace Through Their Drawings

War and conflict have always been an integral part of humankind, posing significant threats to humanity. This article investigates young children’s conceptualisation of war and peace through their drawings. Taking a qualitative, interpretive research paradigm, eight five‐year‐old children who had never experienced war first‐hand were invited to draw pictures depicting their understandings of war and peace accompanied by their narratives. The drawing and talk processes were video‐recorded. Drawing on the theory of social semiotics, this study considers drawing as a multimodal visual artefact and metaphorical representation to analyse the content as illustrated by children. Employing a phenomenological approach, the analysis centres on the meanings, feelings, and constructs of war and peace that the participant children communicated through 25 drawings. The findings indicate that children used visual elements like lines, colours, symbols, and narratives to convey multilayered meaning‐making, where five overarching themes were identified as the children’s conceptualisations of war: concrete depictions and symbols of war and warfare such as weapons and soldiers; descriptions of identifiable actions of war to include fighting, shooting, and killing; the negative consequences of war including dead people and animals, sadness and homelessness; conceptualising peace as the end of war and as a happy, safe place with beautiful nature; and reflections on war and peace including the dichotomy between the two. The findings show that while children who do not have first‐hand experience of war, struggle to fully comprehend its complexity, they still exhibit a basic understanding of the trauma of war. The findings also emphasise the importance of giving voice to children to communicate their understandings and emotions through drawing.

Towards scalable wide area UAS monitoring of forest disturbance using hydrogen powered airships

ABSTRACT Uncrewed Aircraft Systems (UAS) have been recognized as powerful tools for providing monitoring data supporting mitigating and adapting to various abiotic and biotic forest disturbances. However, their suitability and commercial viability have been significantly limited by the necessity for operation primarily within visual line of sight (VLOS). Efforts are currently ongoing to establish frameworks for beyond visual line of sight (BVLOS) UAS operation. This study represents the first successful application of hydrogen-powered BVLOS airship UAS technology for monitoring forest disturbances. The test area covered 1.3 km2 of conserved forest area in North Karelia, Finland, seriously affected by bark beetles and other disturbance agents. Monitoring flights were conducted in spring, summer, and autumn in BVLOS setting operated from a command centre situated approximately 75 km distance from the test area. Deep learning models were applied to study the tree health in the disturbance area, with the special focus on examining the scalability of machine learning models. Multispectral imagery outperformed RGB imagery in classifying trees as healthy, infested, and dead spruce trees, as well as non-spruce tree classes. With multispectral imagery, the F1-scores were 0.88–0.94, 0.80, 0.92–0.96, and 0.75–0.82 for healthy, infested, and dead spruce trees, and non-spruces, respectively. Transfer learning and fine-tuning, which adapted previously trained models to new study areas, improved results compared to training models from scratch and thus contributed to the development of scalable UAS remote sensing methods in mitigating forest disturbances on a larger scale.

The level of skills involved in an observation-based gait analysis.

This study aimed to determine the visual assessment skills during an observation-based gait analysis. Participants (N=40) included 20 physiotherapists (PTs) with>10 years of clinical experience (physiotherapists) and 20 physiotherapy students. Both groups watched a video of the gait of a subject with Guillain-Barré syndrome before and after being provided with information regarding other movements. Further, visual lines were measured using an EMR-8 eye mark recorder, and the results were compared between both groups. The average gaze duration was longer for students than for PTs (F1,79=53.3; p<0.01), whereas PTs gazed more often than the students (F1,79=87.6; p< 0.01). Furthermore, the PTs moved their eyes vertically more often than the students (F1,151=9.1; P< 0.01). We found that being able to discriminate the relative physical relationship of body locations by frequent and rapid vertical gazes could be an indication of the level of skills as an index to express the visual assessment skill in an observation-based gait analysis.

UAV Visual and Thermographic Power Line Detection Using Deep Learning.

Inspecting and maintaining power lines is essential for ensuring the safety, reliability, and efficiency of electrical infrastructure. This process involves regular assessment to identify hazards such as damaged wires, corrosion, or vegetation encroachment, followed by timely maintenance to prevent accidents and power outages. By conducting routine inspections and maintenance, utilities can comply with regulations, enhance operational efficiency, and extend the lifespan of power lines and equipment. Unmanned Aerial Vehicles (UAVs) can play a relevant role in this process by increasing efficiency through rapid coverage of large areas and access to difficult-to-reach locations, enhanced safety by minimizing risks to personnel in hazardous environments, and cost-effectiveness compared to traditional methods. UAVs equipped with sensors such as visual and thermographic cameras enable the accurate collection of high-resolution data, facilitating early detection of defects and other potential issues. To ensure the safety of the autonomous inspection process, UAVs must be capable of performing onboard processing, particularly for detection of power lines and obstacles. In this paper, we address the development of a deep learning approach with YOLOv8 for power line detection based on visual and thermographic images. The developed solution was validated with a UAV during a power line inspection mission, obtaining mAP@0.5 results of over 90.5% on visible images and over 96.9% on thermographic images.

Image Segmentation-Based Oilseed Rape Row Detection for Infield Navigation of Agri-Robot

The segmentation and extraction of oilseed rape crop rows are crucial steps in visual navigation line extraction. Agricultural autonomous navigation robots face challenges in path recognition in field environments due to factors such as complex crop backgrounds and varying light intensities, resulting in poor segmentation and slow detection of navigation lines in oilseed rape crops. Therefore, this paper proposes VC-UNet, a lightweight semantic segmentation model that enhances the U-Net model. Specifically, VGG16 replaces the original backbone feature extraction network of U-Net, Convolutional Block Attention Module (CBAM) are integrated at the upsampling stage to enhance focus on segmentation targets. Furthermore, channel pruning of network convolution layers is employed to optimize and accelerate the model. The crop row trapezoidal ROI regions are delineated using end-to-end vertical projection methods with serialized region thresholds. Then, the centerline of oilseed rape crop rows is fitted using the least squares method. Experimental results demonstrate an average accuracy of 94.11% for the model and an image processing speed of 24.47 fps/s. After transfer learning for soybean and maize crop rows, the average accuracy reaches 91.57%, indicating strong model robustness. The average yaw angle deviation of navigation line extraction is 3.76°, with a pixel average offset of 6.13 pixels. Single image transmission time is 0.009 s, ensuring real-time detection of navigation lines. This study provides upper-level technical support for the deployment of agricultural robots in field trials.

Integrating Null Controllability and Model-Based Safety Assessment for Enhanced Reliability in Drone Design

The increasing use of drones for safety-critical applications, particularly beyond visual lines of sight and over densely populated areas, necessitates safer and more reliable designs. To address this need, this paper introduces a novel methodology integrating Null Controllability with the Model-Based Safety Assessment (MBSA) framework AltaRica 3.0 to optimize propulsor configurations and system architectures. The main advancement of this method lies in the automation of reliability modeling and the integration of controllability assessment, eliminating restrictions on the types of propulsor configurations and system architectures that can be evaluated and significantly reducing the effort required for each design iteration. Through a hexarotor drone case study, the proposed method enabled a high number of design iterations, efficiently exploring various aspects of the design problem simultaneously, such as configuration, system architecture, and controllability hypothesis, which is not possible with state-of-the-art techniques. This approach demonstrated significant reliability improvements by implementing and optimizing redundancies, reducing the probability of loss of control by up to 99%. The case study also highlighted the increasing difficulty of enhancing reliability with each iteration and confirmed that it is unnecessary to consider more than two simultaneous failures for design optimization. A comparison of reliability figures with previous studies highlights the crucial role of system architecture in effectively enhancing drone design reliability. This work advances the field by providing an effective multidisciplinary modeling framework for drone design, enhancing reliability in safety-critical applications.

Development and evaluation of the model for acute kidney injury in patients with cardiac arrest after successful resuscitation

BackgroundThis study aims to construct a clinical prediction model and create a visual line chart depicting the risk of acute kidney injury (AKI) following resuscitation in cardiac arrest (CA) patients. Additionally, the study aims to validate the clinical predictive accuracy of the developed model.MethodsData were retrieved from the Dryad database, and publicly shared data were downloaded. This retrospective cohort study included 347 successfully resuscitated patients post-cardiac arrest from the Dryad database. Demographic and clinical data of patients in the database, along with their renal function during hospitalization, were included. Through data analysis, the study aimed to explore the relevant influencing factors of acute kidney injury (AKI) in patients after cardiopulmonary resuscitation. The study constructed a line chart prediction model using multivariate logistic regression analysis with post-resuscitation shock status (Post-resuscitation shock refers to the condition where, following successful cardiopulmonary resuscitation after cardiac arrest, some patients develop cardiogenic shock.), C reactive protein (CRP), Lactate dehydrogenase (LDH), and Alkaline phosphatase (ALP) identified as predictive factors. The predictive efficiency of the fitted model was evaluated by the area under the curve (AUC) of the receiver operating characteristic (ROC) curve.ResultsMultivariate logistic regression analysis showed that post-resuscitation shock status, CRP, LDH, and PAL were the influencing factors of AKI after resuscitation in CA patients. The calibration curve test indicated that the prediction model was well-calibrated, and the results of the Decision Curve Analysis (DCA) demonstrated the clinical utility of the model constructed in this study.ConclusionPost-resuscitation shock status, CRP, LDH, and ALPare the influencing factors for AKI after resuscitation in CA patients. The clinical prediction model constructed based on the above indicators has good clinical discriminability and practicality.

An Intelligent Bait Delivery Control Method for Flight Vehicle Evasion Based on Reinforcement Learning

During aerial combat, when an aircraft is facing an infrared air-to-air missile strike, infrared baiting technology is an important means of penetration, and the strategy of effective delivery of infrared bait is critical. To address this issue, this study proposes an improved deep deterministic policy gradient (DDPG) algorithm-based intelligent bait-dropping control method. Firstly, by modeling the relative motion between aircraft, bait, and incoming missiles, the Markov decision process of aircraft-bait-missile infrared effect was constructed with visual distance and line of sight angle as states. Then, the DDPG algorithm was improved by means of pre-training and classification sampling. Significantly, the infrared bait-dropping decision network was trained through interaction with the environment and iterative learning, which led to the development of the bait-dropping strategy. Finally, the corresponding environment was transferred to the Nvidia Jetson TX2 embedded platform for comparative testing. The simulation results showed that the convergence speed of this method was 46.3% faster than the traditional DDPG algorithm. More importantly, it was able to generate an effective bait-throwing strategy, enabling the aircraft to successfully evade the attack of the incoming missile. The strategy instruction generation time is only about 2.5 ms, giving it the ability to make online decisions.

The effects of drone transportation on blood component quality: A prospective randomised controlled laboratory study.

The use of uncrewed aerial vehicles (drones) has increased over the last decade. However, their application in healthcare has not been fully examined, in part, due to regulations preventing flight beyond the visual line of sight. This prospective randomised controlled laboratory study aimed to determine whether the invitro quality of packed red blood cell components is maintained when transported by drone, beyond visual line of sight. Ten identical pairs of packed red blood cell units were randomly allocated to transport by drone or by ground vehicle (1:1, allocation concealment) 68 km between two hospitals in Northumbria, UK. Markers of blood component quality were compared at 8, 14, 28 and 35 days following blood unit manufacture. There was no statistical difference in haemolysis, potassium concentration, total haemoglobin, glucose and lactate, haematocrit and mean cell volume, between the two groups, up to the date of unit expiry. The temperature of the packed red blood cell units did not deviate outside the recommended 2-10°C for transportation, regardless of the allocated group. Blood component transport was faster by drone, but did not reach statistical significance. This study demonstrates the feasibility and safety of flying blood components by drone between hospitals in the United Kingdom.

Optimized Radio Frequency Footprint Identification Based on UAV Telemetry Radios.

With the widespread use of unmanned aerial vehicles (UAVs), the detection and identification of UAVs is a vital security issue for the safety of airspace and ground facilities in the no-fly zone. Telemetry radios are important wireless communication devices for UAVs, especially in UAVs beyond the visual line of sight (BVLOS) operating mode. This work focuses on the UAV identification approach using transient signals from UAV telemetry radios instead of the signals from UAV controllers that the former research work depended on. In our novel UAV Radio Frequency (RF) identification system framework based on telemetry radio signals, the EC-α algorithm is optimized to detect the starting point of the UAV transient signal and the detection accuracy at different signal-to-noise ratios (SNR) is evaluated. In the training stage, the Convolutional Neural Network (CNN) model is trained to extract features from raw I/Q data of the transient signals with different waveforms. Its architecture and hyperparameters are analyzed and optimized. In the identification stage, the extracted transient signals are clustered through the Self-Organizing Map (SOM) algorithm and the Clustering Signals Joint Identification (CSJI) algorithm is proposed to improve the accuracy of RF fingerprint identification. To evaluate the performance of our proposed approach, we design a testbed, including two UAVs as the flight platform, a Universal Software Radio Peripheral (USRP) as the receiver, and 20 telemetry radios with the same model as targets for identification. Indoor test results show that the optimized identification approach achieves an average accuracy of 92.3% at 30 dB. In comparison, the identification accuracy of SVM and KNN is 69.7% and 74.5%, respectively, at the same SNR condition. Extensive experiments are conducted outdoors to demonstrate the feasibility of this approach.

Factual Analysis of Factors Influencing Consumer Cognitive Thinking and Automobile Designing using Fuzzy-AHP

Consumer cognitive and emotional responses significantly impact consumers’ affinity for both tangible and intangible products. Recent research underscores the heightened influence of consumer emotional and cognitive perceptions in product design, surpassing reliance on designers’ instincts or experiences. This case study delved into the analysis of automobiles, using cars as focal product. While visual elements like curve lines, grill design, and color predominantly shape consumer perspectives, non-visual factors such as reliability, quality, and ergonomics play a pivotal role in purchasing decisions. Our investigation, involving six industrial experts and over 130 participants, explored non-visual factors affecting consumers’ cognitive perceptions during car purchasing. Through a two-phase experimental approach, we developed a framework to understand the disparities in ranking assigned by consumers and designers to these non-visual factors, revealing perception gaps. Utilizing the rank value methodology, the Pareto principle, and Fuzzy-AHP analysis, we identified mileage/fuel efficiency, safety features, and reliability as dominant factors influencing consumer perceptions. Designers, on the other hand, prioritize safety features, reliability, and quality/warranty. Our findings emphasize the strategic significance for automobile companies to enhance their success rates by prioritizing these key factors, ultimately elevating brand value, profile, and overall financial prosperity.

Risks of Multipath Signal Propagation From GPS Satellites in Mountainous Areas During the Use of UAV as Supplementary Device for HEMS or SAR

It cannot be disputed that using unmanned aerial vehicles as a supplementary search and rescue component is still increasingly being discussed. Several approaches to solving the problem can be stated - buying specially designed unmanned aerial vehicles for HEMS (Helicopter Emergency Medical Service)/SAR (Search and Rescue) or modifying conventional UAVs to supplementary devices for HEMS or SAR. Regardless of which alternative the end-user prefers, the navigation method and determining the current position in space stays unchanged - primarily, the navigation signals from GNSS (Global Navigation Satellite Systems) satellites are used. However, this method can be loaded by the error caused by environmental conditions in which the rescue UAV moves, such as mountains, valleys, specific flora, trees, and much more. All of that causes attenuation, loss or multipath propagation of the signal. In the case of the remote-controlled flight of the UAV (by a pilot-operator or operator itself from the ground station) and when the rule of direct visibility (Visual Line of Sight - VLOS) is used for an unmanned aerial vehicle, this may not be a considerable problem. However, during an automated or fully autonomous flight and the flight of an unmanned aerial vehicle beyond direct visibility (Beyond Visual Line of Sight - BVLOS), knowledge about the exact position of the UAV in space is crucial. The presented article is dedicated to verify the claim about the significance of the problem of multipath signal propagation from the GPS (Global Positioning System) satellite in a selected mountain area, The Little Cold Valley in High Tatras, Slovak Republic.