Search Results (22)

Reverse engineering of printed circuit boards is still largely manual when original computer-aided design documentation is unavailable. This paper presents a semi-automatic vision-based pipeline that prepares an editable KiCad schematic draft for use in an Electronic Design Automation (EDA) workflow from paired TOP and BOTTOM board images. The method combines color-profile estimation, pad and through-hole detection, trace segmentation, optical character recognition, component inference, an explicit evidence graph and schematic export with drawn wires. A separate readability step aligns symbols to a grid and reroutes the reconstructed nets with orthogonal wires; it does not change the reconstructed netlist. The primary quantitative evaluation used twelve synthetic KiCad fixtures and three solver configurations: the default sequential pipeline, an opt-in global component solver and an opt-in probabilistic contact solver. These fixtures provide controlled regression cases and are complemented by a small exploratory acquisition trial on real photographed boards. All configurations completed all runs and passed the export round-trip validation without falling back to label-only connectivity. This round-trip check confirms consistency between the internal reconstruction and the exported schematic, but it is reported separately from electrical correctness against the KiCad reference design. The stricter reconstruction-quality criterion still failed on four stress cases involving repeated component chains, long meandering variable-width traces, circular distractors near pads and two-sided transistor layouts. The probabilistic contact solver was therefore kept as an opt-in diagnostic mode rather than enabled by default; it reduced the global pin-to-pin netlist edit distance from 642 to 525 while preserving schematic export checks. The real-board trial indicates that pad and hole detection can transfer to simple photographs, with trace extraction remaining sensitive to uncontrolled illumination and weak copper contrast. The results support the use of the system as a human-in-the-loop reconstruction assistant and identify component grouping, trace-contact reasoning, real-photograph benchmarking and safe missing-edge activation as the main remaining research problems. Full article

Murals are not merely independent visual artworks. Rather, they are an integral part of architectural heritage, directly attached to buildings’ structural elements, such as brick walls and vaults. However, murals are susceptible to various building-related types of damage, including structural cracks and moisture-induced peeling, due to long-term exposure to environmental factors and geological changes. As the progressive deterioration of these murals hastens the loss of mural value, professional assessment and restoration are urgently required. To tackle the issues of low efficiency in traditional structural damage detection and the absence of predictable repair plans, this paper presents a semi-automatic building-mural protection solution that integrates morphological assessment of mural deterioration with computer vision technology. This study establishes an image prediction system that integrates intelligent damage identification with virtual restoration. First, employing the PaddleSeg deep learning framework and the DeepLabv3 semantic segmentation model, this study used existing mural damage datasets to build a recognition model. The model allows for intelligent identification and labeling of multiple damage types. Subsequently, relying on the ComfyUI platform, Stable Diffusion was used to construct a virtual restoration model. LoRA (low-rank adaptation) technology was introduced to fine-tune the model specifically for the mural style, thus enhancing the directivity and accuracy of virtual restoration. Finally, by applying the results of the recognition model to the virtual restoration model, this study built an integrated system for mural damage diagnosis and virtual restoration. The results show that the damage recognition model achieved a mean intersection over union (mIoU) of 47.8% and a pixel accuracy of 77.97% on the test set, validating the feasibility of using semantic segmentation for mural damage detection. This study presents an integrated workflow framework integrating automatic damage identification and intelligent repair. As an expert-assisted tool, this framework shows application potential for preliminary exploration of mural disease diagnosis and virtual restoration plans, providing technical references for the digital protection of cultural heritage. Full article

Background: Radiomic analyses have been extensively explored in oncologic imaging and more recently in neuroimaging. However, radiomic characterization of the crystalline lens using computed tomography has not yet been systematically investigated. Methods: In this retrospective study, semiautomatic segmentation of the eye lens on orbital CT was performed on 112 patients (mean age 48 ± 20 years, 38% female). After radiomics feature extraction, a Boruta feature selection approach based on the random forest algorithm was applied to select the most relevant radiomics features. Severity of white matter lesions were graded according to the Fazekas scale for each patient on axial non-contrast head CT. Results: In total, 17 important features were associated with age-related changes in the eye lens and three important radiomic features for the differentiation between patients with a Fazekas score > 1 and a control group. Significantly higher values were found in patients with a Fazekas score > 1 compared to the control group regarding all three features, “ClusterShade”, “Skewness” and “DifferenceVariance” (p = 0.0006, 0.0023 and 0.0376, respectively), which are all measures of heterogeneity. No important radiomic features of the eye lens were confirmed between patients with and without hypertension. Conclusions: To the best of our knowledge, this is the first study to use CT-based radiomic analysis of the crystalline lens to detect differences among demographic or clinical groups with small vessel disease. The present results might help to expand the range of applications of radiomics regarding ophthalmic (patho-)physiology and suggest a possible new biomarker for systemic vascular diseases. Full article

Discretely monitoring traffic systems and tracking payloads on vehicle targets can be challenging when traversal occurs off main roads where overhead traffic cameras are not present. This work proposes a portable roadside vehicle detection system as part of a solution for tracking traffic along any path. Training semantic segmentation networks to automatically detect specific types of vehicles while ignoring others will allow the user to track payloads present only on certain vehicles of interest, such as train cars or semi-trucks. Different vision sensors offer varying advantages for detecting targets in changing environments and weather conditions. To analyze the benefits of both, corresponding LiDAR and camera data were collected at multiple roadside sites and then trained on separate semantic segmentation networks for object detection. A custom CNN architecture was built to handle highly asymmetric LiDAR data, and a network inspired by DeepLabV3+ was used for camera data. The performance of both networks was evaluated, and showed comparable accuracy. Inferences run on embedded platforms showed real-time execution matching the performance on the training hardware for edge deployments anywhere. Both camera and LiDAR semantic segmentation networks were successful in identifying vehicles of interest from the proposed viewpoint. These highly accurate vehicle detection networks can pair with a tracking mechanism to establish a non-intrusive roadside detection system. Full article

We present an automatic road incident detector characterised by a low computational complexity for easy implementation in affordable devices, automatic adaptability to changes in scenery and road conditions, and automatic detection of the most common incidents (vehicles with abnormal speed, pedestrians or objects falling on the road, vehicles stopped on the shoulder, and detection of kamikaze vehicles). To achieve these goals, different tasks have been addressed: lane segmentation, identification of traffic directions, and elimination of unnecessary objects in the foreground. The proposed system has been tested on a collection of videos recorded in real scenarios with real traffic, including areas with different lighting. Self-adaptability (plug and play) to different scenarios has been tested using videos with significant scene changes. The achieved system can process a minimum of 80 video frames within the camera’s field of view, covering a distance of 400 m, all within a span of 12 s. This capability ensures that vehicles travelling at speeds of 120 km/h are seamlessly detected with more than enough margin. Additionally, our analysis has revealed a substantial improvement in incident detection with respect to previous approaches. Specifically, an increase in accuracy of 2–5% in automatic mode and 2–7% in semi-automatic mode. The proposed classifier module only needs 2.3 MBytes of GPU to carry out the inference, thus allowing implementation in low-cost devices. Full article

The positional deviation of the in-vehicle Automatic Emergency Call System (AECS) under collision conditions brings difficulties for Intelligent Connected Vehicles (ICVs) post rescue operations. Currently, there is a lack of analysis on system operating conditions during collisions in the reliability assessment methods for the Global Navigation Satellite System (GNSS) deployed in the AECS. Therefore, this paper establishes an in-vehicle collision environment simulation model for emergency calls to explore the influence of parameters such as temperature and vibration on Signal-Based In-Vehicle Emergency Call Systems. We also propose environmental limits applicable to comprehensive tests, which can objectively evaluate reliability and provide data support for the AECS bench test through a satellite-signal-based semi-physical simulation, which is subjected to a bench test under different operating conditions. The findings of this study demonstrate that the occurrence of random vibration and impact stress, induced by vibration, exerts considerable disruptive effects on positional signal data during collisions. Consequently, it leads to substantial interference with the accurate detection of post-collision satellite positioning information. When the simulation operates under a 2.4 gRMS vibration condition, the maximum phase noise error in the positioning system is 8.95%, which does not meet the test accuracy requirements. On the other hand, the semi-simulation system is less affected by temperature changes, and at the maximum allowable temperature difference of the equipment, the maximum phase noise error in the simulated signal is 2.12%. Therefore, based on the influence of phase noise variation on the accuracy of the satellite signal simulation, necessary environmental conditions for the test are obtained, including a temperature that is consistent with the maximum operating temperature of the vector generator and a vibration power spectral density (PSD) lower than 1.2 gRMS. Full article

Coldwater streams are crucial habitats for many biota including Salmonidae and Cottidae species that are unable to tolerate warmer water temperatures. Accurate classification of coldwater streams is essential for their conservation, restoration, and management, especially in light of increasing human disturbance and climate change. Coldwater streams receive cooler groundwater inputs and, as a result, typically remain ice-free during the winter. Based on this empirical thermal evidence, we examined the potential of very high-resolution (VHR) satellite and uncrewed aerial system (UAS) imagery to (i) detect coldwater streams using semi-automatic classification versus visual interpretation approaches, (ii) examine the physical factors that contribute to inaccuracies in detecting coldwater habitats, and (iii) use the results to identify inaccuracies in existing thermal stream classification datasets and recommend coverage updates. Due to complex site conditions, semi-automated classification was time consuming and produced low mapping accuracy, while visual interpretation produced better results. VHR imagery detected only the highest quality coldwater streams while lower quality streams that still met the thermal and biological criteria to be classified as coldwater remained undetected. Complex stream and site variables (narrow stream width, canopy cover, terrain shadow, stream covered by ice and drifting snow), image quality (spatial resolution, solar elevation angle), and environmental conditions (ambient temperature prior to image acquisition) make coldwater detection challenging; however, UAS imagery is uniquely suited for mapping very narrow streams and can bridge the gap between field data and satellite imagery. Field-collected water temperatures and stream habitat and fish community inventories may be necessary to overcome these challenges and allow validation of remote sensing results. We detected >30 km of coldwater streams that are currently misclassified as warmwater. Overall, visual interpretation of VHR imagery it is a relatively quick and inexpensive approach to detect the location and extent of coldwater stream resources and could be used to develop field monitoring programs to confirm location and extent of coldwater aquatic resources. Full article

Reversing climate change requires broad, cohesive, and strategic plans for the mitigation of greenhouse gas emissions from animal farming. The implementation and evaluation of such plans demand accurate and accessible methods for monitoring on-field CH₄ concentration in eructating breath. Therefore, this paper describes a longitudinal study over six months, aiming to test a protocol using a laser methane detector (LMD) to monitor CH₄ emissions in semi-extensive dairy farm systems. Over 10 time points, CH₄ measurements were performed in dry (late gestation) and lactating cows at an Azorean dairy farm. Methane traits including CH₄ concentration related to eructation (E_CH4) and respiration (R_CH₄), and eructation events, were automatically computed from CH₄ measured values using algorithms created for peak detection and analysis. Daily CH₄ emission was estimated from each profile’s mean CH₄ concentration (MEAN_CH₄). Data were analyzed using a linear mixed model, including breed, lactation stage, and parity as fixed effects, and cow (subject) and time point as random effects. The results showed that Holsteins had higher E_CH₄ than Jersey cows (p < 0.001). Although a breed-related trend was found in daily CH₄ emission (p = 0.060), it was not significant when normalized to daily milk yield (p > 0.05). Methane emissions were lower in dry than in lactation cows (p < 0.05) and increased with the advancement of the lactation, even when normalizing it to daily milk yield (p < 0.05). Primiparous cows had lower daily CH₄ emissions related to R_ CH₄ compared to multiparous (p < 0.001). This allowed the identification of periods of higher CH₄ emissions within the milk production cycle of dairy cows, and thus, the opportunity to tailor mitigation strategies accordingly. Full article

Since the 1990s, Morocco’s agriculture has been characterized by the co-existence and transformation of both modern and traditional smallholder systems. In the Atlas Mountains, the effects of rural–urban transformation have led to intensified irrigated agriculture in some agricultural areas, while others were abandoned. To better understand these effects, this study aimed at (1) analyzing the land use and land cover (LULC) changes, (2) assessing the structure and dynamics of vegetation, and (3) comparing a Support Vector Machine (SVM) classification approach with a seasonal rules-based approach. We, therefore, employed a semi-automatic supervised classification of LULC using Landsat data from the 1990s to the 2020s to distinguish between Open Canopy Vegetation, Bareland, Forest, and Water. Overall accuracies achieved ranged from 88% to 90% in 1990, 2000, 2010, and 2020. SVM results indicated the share of Bareland as >80% of the landscape in all periods. With the seasonal rules-based approach, 10% less Bareland was detected than with the SVM approach. Our findings indicate the limitation of detecting vegetation reflectance in semi-arid mountainous regions such as that prevailing in Morocco using a single machine learning method. Full article

Circuit breakers (CBs) are mainly designed to interrupt current flow when faults are detected and have been widely used in industrial applications. The existing CBs manufacturing method is semi-automatic and requires a lot of labor. To realize flexible manufacturing, a multi-robot cooperative CBs flexible manufacturing system (CBFMS) is presented in this study. Aiming at the efficiency of the multi-robot cooperative CBFMS key units, a two-arm cooperation robot approach is proposed. The reinforcement learning algorithm is developed to optimize the manufacturing trajectory of the two-arm cooperation robot. To build and optimize the multi-robot cooperative CBFMS, a digital twin (DT) system describing all physical properties of the physical manufacturing plant is constructed for simulation. In the developed DT system, a kinematic control model of the collaboration robot is established. A real-time display of the robot’s trajectory, manufacturing status, and process manufacturing is provided by the data interaction with the physical cell flow between the units. Following this design, a synchronous mapping between the flexible manufacturing DT system of the CBs and the physical workshop is realized, which enables real-time monitoring and management of the physical production line. The experiments’ results show that the manufacturing efficiency, compared with traditional CBs production, is improved by 22%. Moreover, the multi-robot cooperative CBFMS can make process changes according to the production requirements, which can improve the stability of production. Full article

Automatic Identification System (AIS) messages are useful for tracking vessel activity across oceans worldwide using radio links and satellite transceivers. Such data play a significant role in tracking vessel activity and mapping mobility patterns such as those found during fishing activities. Accordingly, this paper proposes a geometric-driven semi-supervised approach for fishing activity detection from AIS data. Through the proposed methodology, it is shown how to explore the information included in the messages to extract features describing the geometry of the vessel route. To this end, we leverage the unsupervised nature of cluster analysis to label the trajectory geometry, highlighting changes in the vessel’s moving pattern, which tends to indicate fishing activity. The labels obtained by the proposed unsupervised approach are used to detect fishing activities, which we approach as a time-series classification task. We propose a solution using recurrent neural networks on AIS data streams with roughly 87% of the overall F-score on the whole trajectories of 50 different unseen fishing vessels. Such results are accompanied by a broad benchmark study assessing the performance of different Recurrent Neural Network (RNN) architectures. In conclusion, this work contributes by proposing a thorough process that includes data preparation, labeling, data modeling, and model validation. Therefore, we present a novel solution for mobility pattern detection that relies upon unfolding the geometry observed in the trajectory. Full article

High-definition (HD) maps determine the location of the vehicle under limited visibility based on the location information of safety signs detected by sensors. If a safety sign disappears or changes, incorrect information may be obtained. Thus, map data must be updated daily to prevent accidents. This study proposes a map update system (MUS) framework that maps objects detected by a road map detection system and the object present in the HD map. Based on traffic safety signs notified by the Korean National Police Agency, 151 types of objects, including traffic signs, traffic lights, and road markings, were annotated manually and semi-automatically. Approximately 3,000,000 annotations were trained based on the you only look once (YOLO) model, suitable for real-time detection by grouping safety signs with similar properties. The object coordinates were then extracted from the mobile mapping system point cloud, and the detection location accuracy was verified by comparing and evaluating the center point of the object detected in the MUS. The performance of the groups with and without specified properties was compared and their effectiveness was verified based on the dataset configuration. A model trained with a Korean road traffic dataset on our testbed achieved a group model of 95% mAP and no group model of 70.9% mAP. Full article

High mountain environments have shown substantial geomorphological changes forced by rising temperatures in recent decades. As such, paraglacial transition zones in catchments with rapidly retreating glaciers and abundant sediments are key elements in high alpine river systems and promise to be revealing, yet challenging, areas of investigation for the quantification of current and future sediment transport. In this study, we explore the potential of semi-automatic image analysis to detect the extent of the inundation area and corresponding inundation frequency in a proglacial outwash plain (Jamtal valley, Austria) from terrestrial time-lapse imagery. We cumulated all available records of the inundated area from 2018–2020 and analyzed the spatial and temporal patterns of flood flows. The approach presented here allows semi-automated monitoring of fundamental hydrological/hydraulic processes in an environment of scarce data. Runoff events and their intensity were quantified and attributed to either pronounced ablation, heavy precipitation, or a combination of both. We detected an increasing degree of channel concentration within the observation period. The maximum inundation from one event alone took up 35% of the analyzed area. About 10% of the observed area presented inundation in 60–70% of the analyzed images. In contrast, 60–70% of the observed area was inundated in less than 10% of the analyzed period. Despite some limitations in terms of image classification, prevailing weather conditions and illumination, the derived inundation frequency maps provide novel insights into the evolution of the proglacial channel network. Full article

As the complexity and scale of the network environment increase continuously, various methods to detect attacks and intrusions from network traffic by classifying normal and abnormal network behaviors show their limitations. The number of network traffic signatures is increasing exponentially to the extent that semi-realtime detection is not possible. However, machine learning-based intrusion detection only gives simple guidelines as simple contents of security events. This is why security data for a specific environment cannot be configured due to data noise, diversification, and continuous alteration of a system and network environments. Although machine learning is performed and evaluated using a generalized data set, its performance is expected to be similar in that specific network environment only. In this study, we propose a high-speed outlier detection method for a network dataset to customize the dataset in real-time for a continuously changing network environment. The proposed method uses an ensemble-based noise data filtering model using the voting results of 6 classifiers (decision tree, random forest, support vector machine, naive Bayes, k-nearest neighbors, and logistic regression) to reflect the distribution and various environmental characteristics of datasets. Moreover, to prove the performance of the proposed method, we experimented with the accuracy of attack detection by gradually reducing the noise data in the time series dataset. As a result of the experiment, the proposed method maintains a training dataset of a size capable of semi-real-time learning, which is 10% of the total training dataset, and at the same time, shows the same level of accuracy as a detection model using a large training dataset. The improved research results would be the basis for automatic tuning of network datasets and machine learning that can be applied to special-purpose environments and devices such as ICS environments. Full article

Underwater noise has been identified as a relevant pollution affecting marine ecosystems in different ways. Despite the numerous studies performed over the last few decades regarding the adverse effect of underwater noise on marine life, a lack of knowledge and methodological procedures still exists, and results are often tentative or qualitative. A monitoring methodology for the behavioral response of bluefin tuna (Thunnus thynnus) when exposed to ship and wind turbine operational noises was implemented and tested in a fixed commercial tuna feeding cage in the Mediterranean sea. Fish behavior was continuously monitored, combining synchronized echosounder and video recording systems. Automatic information extracted from acoustical echograms was used to describe tuna reaction to noise in terms of average depth and vertical dimensions of the school and the indicators of swimming speed and tilt direction. Video recordings allowed us to detect changes in swimming patterns. Different kinds of stimuli were considered during bluefin tuna cage monitoring, such as noise generated by feeding boats, wind farm operational noise, and other synthetic signals projected in the medium using a broadband underwater projector. The monitoring system design was revealed as a successful methodological approach to record and quantify reactions to noise. The obtained results suggested that the observed reactions presented a strong relationship with insonification pressure level and time. Behavioral changes associated with noise are difficult to observe, especially in semi-free conditions; thus, the presented approach offered the opportunity to link anthropogenic activity with possible effects on a given marine species, suggesting the possibility of achieving a more realistic framework to assess the impacts of underwater noise on marine animals. Full article