Search Results (316)

Multispectral imaging plays a key role in crop monitoring. A major challenge, however, is spectral band misalignment, which can hinder accurate plant health assessment by distorting the calculation of vegetation indices. This study presents a novel approach for short-range calibration of a multispectral camera, utilizing stereo vision for precise geometric correction of acquired images. By using multispectral camera lenses as binocular pairs, the sensor acquisition distance was estimated, and an alignment model was developed for distances ranging from 500 mm to 1500 mm. The approach relied on selecting the red band image as a reference, while the remaining bands were treated as moving images. The stereo camera calibration algorithm estimated the target distance, enabling the correction of band misalignment through previously developed models. The alignment models were applied to assess the health status of baby leaf crops (Lactuca sativa cv. Maverik) by analyzing spectral indices correlated with chlorophyll content. The results showed that the stereo vision approach used for distance estimation achieved high accuracy, with average reprojection errors of approximately 0.013 pixels (4.485 × 10⁻⁵ mm). Additionally, the proposed linear model was able to explain reasonably the effect of distance on alignment offsets. The overall performance of the proposed experimental alignment models was satisfactory, with offset errors on the bands less than 3 pixels. Despite the results being not yet sufficiently robust for a fully predictive model of chlorophyll content in plants, the analysis of vegetation indices demonstrated a clear distinction between healthy and unhealthy plants. Full article

Traditional detection and tracking methods struggle with the complex and dynamic maritime environment due to their poor generalization capabilities. To address this, this paper improves the YOLOv5 network by integrating Transformer and a Convolutional Block Attention Module (CBAM) with the multi-frame image information obtained from radar scans. It proposes a detection and tracking method based on the Detection Tracking Network (DTNet), which leverages transfer learning and the DeepSORT tracking algorithm, enhancing the detection capabilities of the model across various maritime environments. First, radar echoes are preprocessed to create a dataset of Plan Position Indicator (PPI) images for different marine conditions. An integrated network for detecting and tracking maritime targets is then designed, utilizing the feature differences between moving targets and sea clutter, along with the coherence of inter-frame information for moving targets, to achieve multi-target detection and tracking. The proposed method was validated on real maritime targets, achieving a precision of 99.06%, which is a 7.36 percentage point improvement over the original YOLOv5, demonstrating superior detection and tracking performance. Additionally, the impact of maritime regions and weather conditions is discussed, showing that, when transferring from Region I to Regions II and III, the precision reached 92.2% and 89%, respectively, and, when facing rainy weather, although there was interference from the sea clutter and rain clutter, the precision was still able to reach 82.4%, indicating strong generalization capabilities compared to the original YOLOv5 network. Full article

Automatic navigation, as one of the modern technologies in farming automation, enables unmanned driving and operation of agricultural vehicles. In this research, the ZPTM (Zigzag Path Tracking Method) was proposed to reduce the complexity of path planning by using a point cloud consisting of a series of anchor points with spatial information, which are obtained from orthophotos taken by UAVs (Unmanned Aerial Vehicles) to represent the curved path in the zigzag. A local straight path was created by linking two adjacent anchor points, forming the local target path to be tracked, which simplified the navigation algorithm for zigzag path tracking. A nonlinear feedback function was established, using both lateral and heading errors as inputs for determining the desired heading angle of agricultural vehicles, which were guided along the local target path with minimal errors. A GUI (Graphic User Interface) was designed on the navigation terminal to visualize and monitor the working process of agricultural vehicles in automatic navigation, displaying interactive controls and components, including representations of the zigzag path and the agricultural vehicle using affine transformation. A high-clearance sprayer equipped with an automatic navigation system was utilized as the test platform to evaluate the proposed ZPTM. Zigzag navigation tests were conducted to explore the impact of path tracking parameters, including path curvature, moving speed, and spacing between anchor points, on zigzag navigation performance. Based on these tests, a regression model was established to optimize these parameters for achieving accurate and smooth movement. Field test results showed that the maximum error, average error, and RMS (Root Mean Square) error in the zigzag navigation were 3.30 cm, 2.04 cm, and 2.27 cm, respectively. These results indicate that the point cloud path-based ZPTM in this research demonstrates adequate stability, accuracy, and applicability in zigzag navigation. Full article

Underwater gliders are a new type of unmanned underwater vehicle, characterized by high energy efficiency, long endurance, and low operational costs. They hold broad application prospects in fields such as ocean exploration, resource surveying, maritime surveillance, and military defense. This paper takes underwater gliders as the research subject, analyzing the characteristics of magnetic interference signals under different operational conditions. The study found that during full operational states, the motor’s operation generates interference signals at 17 Hz; during attitude adjustment, the movement of the moving block generates significant interference magnetic fields, especially during the forward and backward motion of the block, where interference signals at 20 Hz are particularly pronounced. To meet the objective of equipping underwater gliders with magnetic field sensors for underwater target detection, this paper proposes an adaptive filtering method based on the Recursive Least Squares (RLS) algorithm. The experimental results indicate that after filtering with the RLS algorithm, the amplitude of the noise signal has been reduced by over 60%, and it can effectively eliminate the noise components at 17 Hz and 20 Hz caused by the glider’s motor. This algorithm achieves an average increase in the signal-to-noise ratio (SNR) of 12 dB, which is equivalent to an approximately 80% improvement in accuracy. It significantly enhances the stability and signal-to-noise ratio of the magnetic field signals of underwater targets. This provides a feasible solution for equipping underwater gliders with magnetic field sensors for underwater target detection, holding important practical engineering significance. Full article

To meet the 2050 targets about climate change and decarbonization, accomplishing thermal comfort, Internet of Things (IoT) ecosystems are key enabling technologies to move the Built Environment (BE) towards Smart Built Environment (SBE). The first contributions of this paper conceptualise SBE from its dynamic and adaptative perspectives, considering the human habitat, and enunciate SBE as a multidimensional approach through six ways of inhabiting: defensive, projective, scientific, thermodynamic, subjective, and complex. From these premises, to analyse the performance indicators that characterise these multidisciplinary ways of inhabiting, an IoT-driven methodology is proposed: to deploy a sensor infrastructure to acquire experimental measurements; analyse data to convert them into context-aware information; and make knowledge-based decisions. Thus, this work tackles the inefficiency and high energy consumption of public buildings with the challenge of balancing energy efficiency and user comfort in dynamic scenarios. As current systems lack real-time adaptability, this work integrates an IoT-driven approach to enhance energy management and reduce discrepancies between measured temperatures and normative thresholds. Following the energy efficiency directives, the obtained results contribute to the following: understanding the complexity of the SBE by analysing its thermal performance, quantifying the potential of energy saving, and estimating its economic impact. The derived conclusions show that IoT-driven solutions allow the generation of real-data-based models on which to enhance SBE knowledge, by increasing energy efficiency and guaranteeing user comfort while minimising environmental effects and economic impact. Full article

In complex and dynamic environments, traditional motion detection techniques that rely on visual feature extraction face significant challenges when detecting and tracking small-sized moving objects. These difficulties primarily stem from the limited feature information inherent in small objects and the substantial interference caused by irrelevant information in complex backgrounds. Inspired by the intricate mechanisms for detecting small moving objects in insect brains, some bio-inspired systems have been designed to identify small moving objects in dynamic natural backgrounds. While these insect-inspired systems can effectively utilize motion information for object detection, they still suffer from limitations in suppressing complex background interference and accurately segmenting small objects, leading to a high rate of false positives from the complex background in their detection results. To overcome this limitation, inspired by insect visual neural structures, we propose a novel dual-channel visual network. The network first utilizes a motion detection channel to extract the target’s motion position information and track its trajectory. Simultaneously, a contrast detection channel extracts the target’s local contrast information. Then, based on the target’s motion trajectory, we determine the temporal variation trajectory of the target’s contrast. Finally, by comparing the temporal fluctuation characteristics of the contrast between the target and background false positives, the network can effectively distinguish between the target and background, thereby suppressing false positives. The experimental results show that the visual network performs excellently in terms of detection rate and precision, with an average detection rate of 0.81 and an average precision as high as 0.0968, which are significantly better than those of other comparative methods. This indicates that it has a significant advantage in suppressing false alarms and identifying small targets in complex dynamic environments. Full article

In traffic surveillance systems, accurate camera–LiDAR calibration is critical for effective detection and robust environmental recognition. Due to the significant distances at which sensors are positioned to cover extensive areas and minimize blind spots, the calibration search space expands, increasing the complexity of the optimization process. This study proposes a novel target-less calibration method that leverages dynamic objects, specifically, moving vehicles, to constrain the calibration search range and enhance accuracy. To address the challenges of the expanded search space, we employ a genetic algorithm-based optimization technique, which reduces the risk of converging to local optima. Experimental results on both the TUM public dataset and our proprietary dataset indicate that the proposed method achieves high calibration accuracy, which is particularly suitable for traffic surveillance applications requiring wide-area calibration. This approach holds promise for enhancing sensor fusion accuracy in complex surveillance environments. Full article

In dual-channel synthetic aperture radar (SAR) systems, the estimation of the four-dimensional motion parameters of the ground maneuvering target is a critical challenge. In particular, when spatial degrees of freedom are used to enhance the target’s output signal-to-clutter-plus-noise ratio (SCNR), it is possible to have multiple solutions in the parameter estimation of the target. To deal with this issue, a novel algorithm for estimating the motion parameters of ground moving targets in dual-channel SAR systems is proposed in this paper. First, the random sample consensus (RANSAC) and modified adaptive 2D calibration (MA2DC) are used to prevent the target’s phase from being distorted as a result of channel balancing. To address range migration, the RFRT algorithm is introduced to achieve arbitrary-order range migration correction for moving targets, and the generalized scaled Fourier transform (GSCFT) algorithm is applied to estimate the polynomial coefficients of the target. Subsequently, we propose using the synthetic aperture length (SAL) of the target as an independent equation to solve for the four-dimensional parameter information and introduce a windowed maximum SNR method to estimate the SAL. Finally, a closed-form solution for the four-dimensional parameters of ground maneuvering targets is derived. Simulations and real data validate the effectiveness of the proposed algorithm. Full article

Real-time dynamic capture of a single moving target is one of the most crucial and representative tasks in UAV capture problems. This paper proposes a multi-UAV real-time dynamic capture strategy based on a differential game model to address this challenge. In this paper, the dynamic capture problem is divided into two parts: pursuit and capture. First, in the pursuit–evasion problem based on differential games, the capture UAVs and the target UAV are treated as adversarial parties engaged in a game. The current pursuit–evasion state is modeled and analyzed according to varying environmental information, allowing the capture UAVs to quickly track the target UAV. The Nash equilibrium solution in the differential game is optimal for both parties in the pursuit–evasion process. Then, a collaborative multi-UAV closed circular pipeline control method is proposed to ensure an even distribution of capture UAVs around the target, preventing excessive clustering and thereby significantly improving capture efficiency. Finally, simulations and real-flight experiments are conducted on the RflySim platform in typical scenarios to analyze the computational process and verify the effectiveness of the proposed method. Results indicate that this approach effectively provides a solution for multi-UAV dynamic capture and achieves desirable capture outcomes. Full article

The deployment of wildlife fences in Africa serves as a crucial intervention to balance wildlife conservation with human safety and agricultural productivity. This review synthesizes current research and case studies to provide a comprehensive understanding of the implications, benefits, and drawbacks of wildlife fencing in Africa. Information was drawn from 54 articles selected through a thorough search of the Web of Science and Scopus databases. Results indicate that the primary reason for fencing was the mitigation of human–wildlife conflicts. Electric fences were the most commonly mentioned type, prominently used to protect agricultural lands from crop-raiding species. In addition, the prevention of livestock depredation and disease transmission was also an important driver for fencing. Elephants were the most studied species concerning wildlife fencing, and they caused the most damage to fences, creating pathways for other species to move beyond protected areas. Antelopes and large carnivores were also common targets for wildlife fences. Fences were found to be effective mainly against crop raiding particularly when well-maintained through frequent inspections for damages and permeability. Several authors documented challenges in fencing against primates, burrowers, and high-jumping species like leopards. The cost of fences varied depending on the materials, design, and maintenance, significantly impacting local communities near conservation areas. Despite their benefits, wildlife fences posed ecological challenges, such as habitat fragmentation and restricted animal movement, necessitating integrated management approaches that include wildlife corridors and crossing structures. This review provides insights for policymakers and conservationists to optimize the use of fences in the diverse environmental contexts of the African continent. Full article

This research proposes an all-metal metamaterial-based absorber with a novel geometry capable of refractive index sensing in the terahertz (THz) range. The structure consists of four concentric diamond-shaped gold resonators on the top of a gold metal plate; the resonators increase in height by 2 µm moving from the outer to the inner resonators, making the design distinctive. This novel configuration has played a very significant role in achieving multiple ultra-narrow resonant absorption peaks that produce very high sensitivity when employed as a refractive index sensor. Numerical simulations demonstrate that it can achieve six significant ultra-narrow absorption peaks within the frequency range of 5 to 8 THz. The sensor has a maximum absorptivity of 99.98% at 6.97 THz. The proposed absorber also produces very high-quality factors at each resonance. The average sensitivity is 7.57/Refractive Index Unit (THz/RIU), which is significantly high when compared to the current state of the art. This high sensitivity is instrumental in detecting smaller traces of samples that have very correlated refractive indices, like several harmful gases. Hence, the proposed metamaterial-based sensor can be used as a potential gas detector at terahertz frequency. Furthermore, the structure proves to be polarization-insensitive and produces a stable absorption response when the angle of incidence is increased up to 60°. At terahertz wavelength, the proposed design can be used for any value of the aforementioned angles, targeting THz spectroscopy-based biomolecular fingerprint detection and energy harvesting applications. Full article

For the demand for long-range and high-resolution target reconstruction of slow-moving small underwater targets, research on single-photon lidar target reconstruction technology is being carried out. This paper reports the sequential multimodal underwater single-photon lidar adaptive target reconstruction algorithm based on spatiotemporal sequence fusion, which has strong information extraction and noise filtering ability and can reconstruct the target depth and reflective intensity information from complex echo photon time counts and spatial pixel relationships. The method consists of three steps: data preprocessing, sequence-optimized extreme value inference filtering, and collaborative variation strategy for image optimization to achieve high-quality target reconstruction in complex underwater environments. Simulation and test results show that the target reconstruction method outperforms the current imaging algorithms, and the built single-photon lidar system achieves underwater lateral and distance resolution of 5 mm and 2.5cm@6AL, respectively. This indicates that the method has a great advantage in sparse photon counting imaging and possesses the capability of underwater target imaging under the background of strong light noise. It also provides a good solution for underwater target imaging of small slow-moving targets with long-distance and high-resolution. Full article

Although effects of stress-induced anxiety on the gastrointestinal tract and enteric nervous system (ENS) are well studied, how ENS dysfunction impacts behaviour is not well understood. We investigated whether ENS modulation alters anxiety-related behaviour in rats. We used loperamide, a potent μ-opioid receptor agonist that does not cross the blood–brain barrier, to manipulate ENS function and assess changes in behaviour, gut and brain gene expression, and microbiota profile. Sprague Dawley (male/female) rats were acutely dosed with loperamide (subcutaneous) or control solution, and their behavioural phenotype was examined using open field and elevated plus maze tests. Gene expression in the proximal colon, prefrontal cortex, hippocampus, and amygdala was assessed by RNA-seq and caecal microbiota composition determined by shotgun metagenome sequencing. In female rats, loperamide treatment decreased distance moved and frequency of supported rearing, indicating decreased exploratory behaviour and increased anxiety, which was associated with altered hippocampal gene expression. Loperamide altered proximal colon gene expression and microbiome composition in both male and female rats. Our results demonstrate the importance of the ENS for communication between gut and brain for normo-anxious states in female rats and implicate corticotropin-releasing hormone and gamma-aminobutyric acid gene signalling pathways in the hippocampus. This study also sheds light on sexually dimorphic communication between the gut and the brain. Microbiome and colonic gene expression changes likely reflect localised effects of loperamide related to gut dysmotility. These results suggest possible ENS pharmacological targets to alter gut to brain signalling for modulating mood. Full article

Background/Objectives: Ongoing challenges in epilepsy therapy warrant research on alternative treatments that offer improved efficacy and reduced side effects. Designed to enhance mitochondrial targeting and increase bioavailability, mitocurcumin (MitoCur) was evaluated for the first time as an antiepileptic agent, with curcumin (Cur) and sodium valproate (VPA), a standard antiepileptic drug, included for comparison. This study investigated the effects on seizure onset, severity, and progression in a zebrafish model of pentylenetetrazole (PTZ)-induced seizures and measured the concentrations of the compounds in brain tissue. Methods: Zebrafish were pre-treated with MitoCur and Cur (both at 0.25 and 0.5 µM doses) and VPA (0.25 and 0.5 mM) and observed for four minutes to establish baseline locomotor behavior. Subsequently, the animals were exposed to a 5 mM PTZ solution for 10 min, during which seizure progression was observed and scored as follows: 1—increased swimming; 2—burst swimming, left and right movements; 3—circular movements; 4—clonic seizure-like behavior; 5—loss of body posture. The studied compounds were quantified in brain tissue through HPLC and LC-MS. Results: Compared to the control group, all treatments reduced the distance moved and the average velocity, without significant differences between compounds or doses. During PTZ exposure, seizure latencies revealed that all treatments effectively delayed seizure onset up to score 4, demonstrating efficacy in managing moderate seizure activity. Notably, MitoCur also provided significant protection against the most severe seizure score (score 5). Brain tissue uptake analysis indicated that MitoCur achieved higher concentrations in the brain compared to Cur, at both doses. Conclusions: These results highlight the potential of MitoCur as a candidate for seizure management. Full article

For applications such as home surveillance systems and assisted living for elderly care, sensing capabilities are essential for tasks such as locating, determining the approximate position of a person, or identifying the status of a person (static or moving), since the effects caused by the presence of people can be captured in the power received by signals in an infrastructure deployed for these purposes. Human interference in Received Signal Strength Indicator (RSSI) measurements between different pairs of wireless nodes can vary depending on whether the target is moving or static. To test these ideas, an experiment was conducted using four nodes equipped with the ZigBee protocol in each corner of an empty 6.9 m × 8.1 m × 3.05 m room. These nodes were configured as routers, communicating with a coordinator outside the room that instructed the nodes to send back their pairwise RSSI measurements. The coordinator was connected to a computer in order to log the measurements, as well as the time at which the measurements were generated. The code was run for every iteration of the experiment, whether the target was static, moving, or when the number of targets was increased to five. The data were then statistically analyzed to extract patterns and other target relational parameters. There was a correlation between the change in the pairwise RSSI and the path described by the target when moving through the room. The data presented by the results can aid algorithms for device-free localization and crowd classification, with a low infrastructure cost for both, and shed light on the relevant characteristics correlated with the path and crowd size in indoor settings. Full article