Target Location Research Articles

Frame-based stereotactic biopsies of brainstem lesions – Monocentric comparison of the transfrontal and the suboccipital-transcerebellar approach over a 16-year period

Both the transfrontal and the suboccipital-transcerebellar approach are frequently used trajectories for frame-based stereotactic biopsies of brainstem lesions. Nevertheless, it remains unclear which approach is more favorable in terms of complications, diagnostic success and outcome, especially considering the location of the lesion within the brainstem. This study compared the safety and diagnostic yield of these two approaches. Furthermore, a brainstem zone model was created to answer the question, whether there is a favorable approach depending on the location of the lesion in the brainstem. A retrospective analysis of 84 consecutive cases of frame-based stereotactic biopsies for brainstem lesions via either transfrontal or suboccipital-transcerebellar approaches over a 16-year period was performed. Clinical and surgical data regarding trajectories, histopathology, complications and outcome was collected. The brainstem was divided in anatomical zones to compare the use of the two approaches depending on the location of the lesions. A total of n = 84 cases of stereotactic biopsies for brainstem lesions were performed. In 36 cases the suboccipital-transcerebellar approach was used, while in 48 cases surgery was performed via the transfrontal approach. The patient’s demographic data were comparable between the two approaches. Overall diagnostic yield was 90.5% (93.8% transfrontal vs. 86.1% suboccipital, p = 0.21, Risk Difference (RD) 0.077, CI [-0.0550, 0.2090]). Complications occurred in 11 cases (total complication rate: 13.1%; 12.5% transfrontal vs. 13.9% suboccipital, p = 0.55, RD 0.014, CI [-0.1607, 0.1327]). The brainstem model showed a more frequent use of the suboccipital approach in lesions of the dorsal pons. The transfrontal approach was used more frequently in mesencephalic targets. No significant differences in terms of complications and diagnostic yield were observed, even though complications in medullary lesions appeared higher using the transfrontal approach. This study showed, that if the approaches are used for their intended target locations there are no significant differences between the transfrontal and the suboccipital-transcerebellar approach for frame-based stereotactic biopsies of brainstem lesions in terms of diagnostic yield and safety. Therefore, our data suggests that both approaches should be considered for stereotactic biopsy of brainstem lesions.

Optimal Beamforming for Secure Integrated Sensing and Communication Exploiting Target Location Distribution

Optimal Beamforming for Secure Integrated Sensing and Communication Exploiting Target Location Distribution

Proprioceptive acuity for locating and controlling movements of a hand-held tool

We investigated proprioceptive acuity for location and motion of a never seen hand-held tool (30 cm long rod) and the accuracy of movements to place tool parts in the location of remembered visual targets. Ten blindfolded right-handed subjects (5 females) reached with the tool held in the right hand to touch the tip and midpoint to the stationary and moving left index-tip, to the right and left ear lobes and to remembered visual target locations. We also tested accuracy of left hand rod reaches to the ear lobes to determine if rod dimensions and control of tool movements experienced during right hand tool use could be used to accurately localize the rod parts when held in the left hand. Errors for right hand-held rod-tip movements to touch the stationary and moving left index-tip averaged only about 1 cm larger than observed previously for right hand movements to touch its index-tip to the left index-tip. The tool-tip was localized with lower mean distance errors (about 1 cm) than the tool-midpoint (5.5–6.5 cm) when reaching to touch the ear lobes with the rod in right and left hands. Right hand reaches to place the tool- tip and midpoint in remembered visual target locations were inaccurate with large overshoots of close targets and undershoots of far targets, similar to previous reports for reaching with the right hand to remembered visual targets. These results support the distalization hypothesis, that tool the tool endpoint becomes the effective upper limb endpoint when using the tool.

Graph-Based Decentralized Task Allocation for Multi-Robot Target Localization

Graph-Based Decentralized Task Allocation for Multi-Robot Target Localization

Low-complexity multi-target localization via multi-BS sensing

Integrated Sensing and Communication (ISAC) is envisioned as a promising technology for Sixth-Generation (6G) wireless communications, which enables simultaneous high-rate communication and high-precision target localization. Compared to independent sensing and communication modules, dual-function ISAC could leverage the strengths of both communication and sensing in order to achieve cooperative gains. When considering the communication core network, ISAC system facilitates multiple communication devices to collaborate for networked sensing. This paper investigates such kind of cooperative ISAC systems with distributed transmitters and receivers to support non-connected and multi-target localization. Specifically, we introduce a Time of Arrival (TOA) based multi-target localization scheme, which leverages the bi-static range measurements between the transmitter, target, and receiver channels in order to achieve elliptical localization. To obtain the low-complexity localization, a two-stage search-refine localization methodology is proposed. In the first stage, we propose a Successive Greedy Grid-Search (SGGS) algorithm and a Successive-Cancellation-List Grid-Search (SCLGS) algorithm to address the Measurement-to-Target Association (MTA) problem with relatively low computational complexity. In the second stage, a linear approximation refinement algorithm is derived to facilitate high-precision localization. Simulation results are presented to validate the effectiveness and superiority of our proposed multi-target localization method.

Research on small target detection algorithm for water surface in complex weather environment

Abstract Detecting small targets on water surfaces in varying weather conditions presents considerable challenges, primarily due to changing lighting and precipitation such as rain or fog. To mitigate these issues, an enhanced YOLOv5 algorithm for the recognition of small targets on water surfaces is proposed. This approach leverages optical flow to trace ship movements and create synthesized frames, thereby improving image clarity via histogram equalization. The algorithm integrates CBAM attention mechanisms and feature pyramid structures, which significantly boost the detection of small targets. Additionally, this method incorporates data augmentation to enlarge the training dataset, coupled with class weight balancing and the SIOU loss function to improve target localization accuracy. Experimental results demonstrate a marked improvement in mIoU, increasing from 0.7538 to 0.8392, thus effectively recognizing small targets in complex environments.

Systematical Sensor Path Optimization Solutions for AOA Target Localization Accuracy Improvement With Theoretical Analysis

Systematical Sensor Path Optimization Solutions for AOA Target Localization Accuracy Improvement With Theoretical Analysis

Tailhook Recognition for Carrier-Based Aircraft Based on YOLO with Bi-Level Routing Attention

To address the problems of missed and false detections caused by target occlusion and lighting variations, this paper proposes a recognition model based on YOLOv5 with bi-level routing attention to achieve precise real-time small object recognition, using the problem of tailhook recognition for carrier-based aircraft as a representative application. Firstly, a module called D_C3, which combines deformable convolution, was integrated into the backbone network to enhance the model’s learning ability and adaptability in specific scenes. Secondly, a bi-level routing attention mechanism was employed to dynamically focus on the regions of the feature map that are more likely to contain the target, leading to more accurate target localization and classification. Additionally, the loss function was optimized to accelerate the bounding box regression process. The experimental results on the self-constructed CATHR-DET and the public VOC dataset demonstrate that the proposed method outperforms the baselines in overall performance.

Avelumab reduces STAT3 expression with effects on IL-17RA and CD15.

Avelumab is a human antibody that targets the programmed cell death ligand-1 (PD-L1) protein in cancer cells. Novel anticancer therapies for renal cell carcinoma (RCC) consider cluster of differentiation 15 (CD15) and interleukin 17 receptor A (IL-17RA) as potential targets. Notably, the expression of PD-L1, CD15 and IL-17RA is dependent on signal transducer and activator of transcription 3 (STAT3). The aim of the study was to investigate whether targeting PD-L1 with avelumab alters the expression levels of CD15 and IL-17RA, and to assess the STAT3-mediated regulation of CD15 and IL-17RA. We applied immunocytochemistry (ICC) and confocal laser scanning (CLS) microscopy to assess the expression and localization of the immunotherapy targets in 3 renal cancer cell lines and 1 healthy renal cell line. After treatment with 20 ng/mL avelumab, renal cancer cells showed a reduction in STAT3 expression. The expression of CD15 increased in cancer cells that exhibited a high level of IL-17RA, and the membrane signal of CD15 was reduced. In other renal cancer cell lines, the expression of CD15 decreased. Conversely, the level of IL-17RA changed only in healthy renal cells after treatment with avelumab, with no impact on renal cancer cells. Our study suggests that the targeting of PD-L1 with avelumab alters the expression of CD15 and IL-17RA, which play an important prognostic and therapeutic role in novel anticancer therapy.

Barrier-free tomato fruit selection and location based on optimized semantic segmentation and obstacle perception algorithm.

With the advancement of computer vision technology, vision-based target perception has emerged as a predominant approach for harvesting robots to identify and locate fruits. However, little attention has been paid to the fact that fruits may be obscured by stems or other objects. In order to improve the vision detection ability of fruit harvesting robot, a fruit target selection and location approach considering obstacle perception was proposed. To enrich the dataset for tomato harvesting, synthetic data were generated by rendering a 3D simulated model of the tomato greenhouse environment, and automatically producing corresponding pixel-level semantic segmentation labels. An attention-based spatial-relationship feature extraction module (SFM) with lower computational complexity was designed to enhance the ability of semantic segmentation network DeepLab v3+ in accurately segmenting linear-structured obstructions such as stems and wires. An adaptive K-means clustering method was developed to distinguish individual instances of fruits. Furthermore, a barrier-free fruit selection algorithm that integrates information of obstacles and fruit instances was proposed to identify the closest and largest non-occluded fruit as the optimal picking target. The improved semantic segmentation network exhibited enhanced performance, achieving an accuracy of 96.75%. Notably, the Intersection-over-Union (IoU) of wire and stem classes was improved by 5.0% and 2.3%, respectively. Our target selection method demonstrated accurate identification of obstacle types (96.15%) and effectively excluding fruits obstructed by strongly resistant objects (86.67%). Compared to the fruit detection method without visual obstacle avoidance (Yolo v5), our approach exhibited an 18.9% increase in selection precision and a 1.3% reduction in location error. The improved semantic segmentation algorithm significantly increased the segmentation accuracy of linear-structured obstacles, and the obstacle perception algorithm effectively avoided occluded fruits. The proposed method demonstrated an appreciable ability in precisely selecting and locating barrier-free fruits within non-structural environments, especially avoiding fruits obscured by stems or wires. This approach provides a more reliable and practical solution for fruit selection and localization for harvesting robots, while also being applicable to other fruits and vegetables such as sweet peppers and kiwis.

Lomba Kreativitas Olahan Makanan Berbahan Dasar Porang bagi Ibu-Ibu PKK di Desa Karangpatihan, Kabupaten Ponorogo

Abstract The creativity of the community in processing various types of porang-based foods is needed. Porang is a local Indonesian commodity that has enormous potential to be exported and sold in the international market. Porang tubers are the raw material for making shirataki rice which has a very high selling price. To achieve this goal, training in making processed food from porang is required. This community service programme focuses on training assistance in porang-based food processing. To see how much the community understands after receiving training, on the third day a porang-based food cooking competition was held to motivate residents to make new innovations related to porang food recipes that they develop. The target location for this community service programme is in Karangpatihan Village, Pulung District, Ponorogo Regency by targeting PKK mothers in four hamlets including Jurugan, Selodono, Krajan, and Dungus. This community service activity was closed with a focus group discussion (FGD) to listen to the aspirations of PKK women and see the future of porang in the region. As is the case in several other areas, the constraints of the community in processing porang are related to the processing of porang which is still quite complicated. In addition, the itchiness caused by holding porang in the hand.

Multi‐scale feature extraction for energy‐efficient object detection in remote sensing images

AbstractObject detection in remote sensing images aims to interpret images to obtain information on the category and location of potential targets, which is of great importance in traffic detection, marine supervision, and space reconnaissance. However, the complex backgrounds and large scale variations in remote sensing images present significant challenges. Traditional methods relied mainly on image filtering or feature descriptor methods to extract features, resulting in underperformance. Deep learning methods, especially one‐stage detectors, for example, the Real‐Time Object Detector (RTMDet) offers advanced solutions with efficient network architectures. Nevertheless, difficulty in feature extraction from complex backgrounds and target localisation in scale variations images limits detection accuracy. In this paper, an improved detector based on RTMDet, called the Multi‐Scale Feature Extraction‐assist RTMDet (MRTMDet), is proposed which address limitations through enhancement feature extraction and fusion networks. At the core of MRTMDet is a new backbone network MobileViT++ and a feature fusion network SFC‐FPN, which enhances the model's ability to capture global and multi‐scale features by carefully designing a hybrid feature processing unit of CNN and a transformer based on vision transformer (ViT) and poly‐scale convolution (PSConv), respectively. The experiment in DIOR‐R demonstrated that MRTMDet achieves competitive performance of 62.2% mAP, balancing precision with a lightweight design.

Two-Dimensional Scattering Center Estimation for Radar Target Recognition Based on Multiple High-Resolution Range Profiles.

A new estimation strategy on locations of two-dimensional target scattering centers for radar target recognition is developed by using multiple high-resolution range profiles (HRRPs). Based on the range information contained in multiple HRRPs obtained from various observation angles, the estimated target scattering centers can be successfully located at the intersection points of the lines passing through the multiple HRRP points. This geometry-based algorithm can significantly reduce the computational complexity while ensuring the ability to estimate the two-dimensional target scattering centers. The computational complexity is formulated and compared to that of the conventional methods based on the synthetic aperture radar (SAR) images and HRRP sequences. In order to verify the performance of the proposed algorithm, the numerical and experimental results for three different types of aircraft were compared to those from SAR images. At the end of this article, the estimated radar scattering centers are used as the target features for the conventional classifier machine to confirm its target classification performance.

Research and Experiment on Miss-Seeding Detection of Potato Planter Based on Improved YOLOv5s

In order to improve the performance of potato planter, reduce miss-seeding rates, enhance the overall quality of the seeding operation, and ultimately increase the yield of the potato, it is necessary to implement effective technical means to monitor and identify the miss-seeding issues during the seeding process. The existing miss-seeding detection technologies commonly use sensors to monitor, but such technologies are easily affected by factors like heavy dust and strong vibrations, resulting in poor interference resistance and adaptability. Therefore, this study aims to explore and apply deep learning algorithms to achieve real-time monitoring of the miss-seeding phenomenon in potato planter during the planting process. Considering both the lightweight of the miss-seeding detection model and its practical deployment, this study selects and adapts the YOLOv5s algorithm to achieve this goal. Firstly, the attention mechanism is integrated into the backbone network to suppress background interference and improve detection accuracy. Secondly, the non-maximum suppression algorithm is improved by replacing the original IoU-NMS with the Soft-NMS algorithm to enhance the bounding box regression rate and reduce missed detections of potato seeds due to background overlap or occlusion. Experimental results show that the accuracy of the improved algorithm in detecting miss-seeding increased from 96.02% to 98.30%, the recall rate increased from 96.31% to 99.40%, and the mean average precision (mAP) improved from 99.12% to 99.40%. The improved model reduces missed and false detections, provides more precise target localization, and is suitable for miss-seeding detection in natural environments for potato planter, providing technical and theoretical support for subsequent intelligent reseeding in potato planter.

The Wolf Spider Tigrosa helluo Uses Visual Associative and Beacon Landmarks During Water Maze Navigation Tasks

ABSTRACTWolf spiders can learn simple spatial navigation tasks. Previous studies have shown that the wolf spider Tigrosa helluo can use environmental edge features (reference frame landmarks) to learn the location of a dry target in flooded T‐mazes; however, the relative importance of different types or numbers of landmark cues to spatial learning remains unknown. We used a modified open arena water maze and recorded the ability of adult female T. helluo to find a target reward (a dark and dry cup) among cups that were identical to the target but flooded. We measured variation in spatial learning by measuring time to target with no landmark (control), with a beacon (a landmark that is part of the target), with an associative cue (a landmark associated with a specific navigational action), and with both a beacon and an associative cue (N = 92 subjects, n = 23 per landmark cue treatment). For each treatment, we tested females for five trials each on four consecutive days, with the last trial on the fourth day having an altered target location, totaling 19 training trials and one reversal trial (1840 trials). We found that spiders took significantly less time to find the target over subsequent trials within a day and learned more quickly when landmark cues were present, but we found no difference in the type or number of landmark features in the meantime to target entrance. After learning a target location, moving the landmark significantly increased the mean time to target entrance in the combined beacon and associative cue treatment relative to other treatments. Our results indicate that wolf spiders use visual beacons and associative cue landmarks alone or in combination and that performance improves across trials when landmarks are present and deteriorates more when multiple landmarks are moved.

AI-empowered Search Drone: Advanced AI-based Tiny Object Search Drone for Broad Areas

Abstract. Detecting the garbage distribution and position in broad terrains(beaches, grasslands, squares, etc) is extremely important for environmental protection and urban image. Manually screen these terrains is time-consuming and ineicient. Autonomous robot may find the wastes automatically, but they may hinder and even endanger the pedestrians. Unmanned aerial systems(UAS) are ideal tools for monitoring broad areas and detecting garbage. In this project we create an AI-empowered Search Drone for extensive garbage search in broad regions. The camera on our drone and the external image transmission module on the computer receive and process top-view image from the sky. The images from high altitude lead to the reduction in target size and object distortion. We innovately modified the network structure of the oicial YOLOv5 model by adding layers specifically for small object detection, enhancing its adaptability to small targets captured by drones.To address camera image distortion, we leveraged the OpenCV (cv2) library to obtain and apply a distortion correction matrix. Furthermore, we devised an algorithm that calculates the real-world location of targets based on their pixel positions, the drones height, and the ratio between pixel and real-world dimensions.To strengthen the models garbage search capabilities, we simulated various garbage search scenarios in open environments and collected image datasets using drones for specialized training. Finally, we utilized the PyQt library to develop a simplistic front-end interface for data analysis and comparison of the detection performance between the original and optimized models. Experimental results demonstrate that the optimized model outperforms the original model, effectively enhancing garbage search eiciency in extensive areas. This project represents a significant step forward in leveraging technology to combat environmental pollution.

Single-photon 3D imaging of room-scale scenes through scattering media.

Light detection and ranging (LiDAR) utilizes eye-safe laser beams to perceive the world in three-dimensional (3D) detail, offering machines and computers with an accurate representation of their surroundings. This technology is widely employed in metrology, environmental monitoring, archaeology, and robotics. However, the presence of scattering media in the optical path, such as fog, dust, or translucent plates, will cause light scattering and occlude direct observation of the scene. To address scattering distortions, conventional methods require the prior knowledge of the scattering media or the target location, limiting their applicability outside the laboratory. Leveraging single-photon sensitivity and time-gated technology, single photon LiDAR emerges as a promising solution for active scattering imaging. In this study, we construct a single-photon LiDAR prototype and demonstrate its capability to perform 3D imaging of a room-scale (1.1 m × 1.1 m × 4 m) hidden scene behind a ground glass diffuser located approximately 50 meters away from the imaging system. Incorporating phase function to construct the forward model and considering the system-induced temporal broadening, our method is capable of producing reliable results behind various scattering layers. The results indicate potential applications such as remote non-invasive testing and detection in challenging scenarios.

Transmission Line Defect Target-Detection Method Based on GR-YOLOv8.

In view of the low levels of speed and precision associated with fault detection in transmission lines using traditional algorithms due to resource constraints, a transmission line fault target-detection method for YOLOv8 (You Only Look Once version 8) based on the Rep (Representational Pyramid) Visual Transformer and incorporating an ultra-lightweight module is proposed. First, the YOLOv8 detection network was built. In order to address the needs of feature redundancy and high levels of network computation, the Rep Visual Transformer module was introduced in the Neck part to integrate the pixel information associated with the entire image through its multi-head self-attention and enable the model to learn more global image features, thereby improving the computational speed of the model; then, a lightweight GSConv (Grouped and Separated Convolution, a combination of grouped convolution and separated convolution) convolution module was added to the Backbone and Neck to share computing resources among channels and reduce computing time and memory consumption, by which the computational cost and detection performance of the detection network were balanced, while the model remained lightweight and maintained its high precision. Secondly, the loss function Wise-IoU (Intelligent IOU) was introduced as the Bounding-Box Regression (BBR) loss function to optimize the predicted bounding boxes in these grid cells and shift them closer to the real target location, which reduced the harmful gradients caused by low-quality examples and further improved the detection precision of the algorithm. Finally, the algorithm was verified using a data set of 3500 images compiled by a power-supply inspection department over the past four years. The experimental results show that, compared with the seven classic and improved algorithms, the recall rate and average precision of the proposed algorithm were improved by 0.058 and 0.053, respectively, compared with the original YOLOv8 detection network; the floating-point operations per second decreased by 2.3; and the picture detection speed was increased to 114.9 FPS.

Research on underwater target signal orientation estimation based on smoothness priors approach

PurposeThis paper aims to address the challenges in hydroacoustic signal detection, signal distortion and target localization caused by baseline drift. The authors propose a combined algorithm that integrates short-time Fourier transform (STFT) detection, smoothness priors approach (SPA), attitude calibration and direction of arrival (DOA) estimation for micro-electro-mechanical system vector hydrophones.Design/methodology/approachInitially, STFT method screens target signals with baseline drift in low signal-to-noise ratio environments, facilitating easier subsequent processing. Next, SPA is applied to the screened target signal, effectively removing the baseline drift, and combined with filtering to improve the signal-to-noise ratio. Then, vector channel amplitudes are corrected using attitude correction with 2D compass data. Finally, the absolute target azimuth is estimated using the minimum variance distortion-free response beamformer.FindingsSimulation and experimental results demonstrate that the SPA outperforms high-pass filtering in removing baseline drift and is comparable to the effectiveness of variational mode decomposition, with significantly shorter processing times, making it more suitable for real-time applications. The detection performance of the STFT method is superior to instantaneous correlation detection and sample entropy methods. The final DOA estimation achieves an accuracy within 2°, enabling precise target azimuth estimation.Originality/valueTo the best of the authors’ knowledge, this study is the first to apply SPA to baseline drift removal in hydroacoustic signals, significantly enhancing the efficiency and accuracy of signal processing. It demonstrates the method’s outstanding performance in the field of underwater signal processing. In addition, it confirms the reliability and feasibility of STFT for signal detection in the presence of baseline drift.

Social and Behavioural Change Communication Challenges, Opportunities and Lessons from Past Public Health Emergencies and Disease Outbreaks: A Scoping Review.

Background: Documentation of social behavioural change communication (SBCC) regarding challenges, opportunities and lessons drawn from past public health emergencies is worthwhile to inform priorities for future response efforts. Aim: The aim of this review is to scope the evidence on social behavioural change communication regarding challenges, opportunities and lessons drawn from Ebola, coronavirus disease 2019 (COVID-19), monkeypox and cholera outbreaks from studies published before March 2024, and suggest priorities for future response efforts. Methods: A Boolean strategy was used to search electronic databases for relevant published articles, complemented by relevant studies identified from reference lists. Results: The challenges, opportunities, lessons learnt and priorities for SBCC were consistent across study contexts, showing marked variations over time. The significance of technology, infodemic management, and behavioural data generation emerged more frequently and became increasingly important over time. Identified challenges were uptake hesitancy, limited capacity to undertake infodemic management, inadequate funding and human resources for SBCC, competing priorities, parallel or conflicting interventions due to inadequate coordination, difficulties evaluating SBCC programmes and missed opportunities for integration into routine programmes. Existing supportive structures for SBCC, strong political will and participation, as well as rapid information exchange enabled by technological advancement, represented opportunities for enhancing the effectiveness of SBCC programmes. Key lessons were that a multisectoral approach and coordination, partnership and active collaboration amongst stakeholders; building/strengthening trust, target population segmentation and localization of interventions, are important for enhancing the effectiveness of SBCC programmes. Political will, involvement and participation represent the core of social behavioural change (communication) interventions during a public health emergency. Conclusion: SBCC programming for future response to public health emergencies and disease outbreaks should consider the diverse assortment of benefits, threats/challenges and opportunities brought about by technology, infodemics and behavioural data generation to be more effective.