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    <pageLast/>
    <pageNumber>15</pageNumber>
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    <issue>1</issue>
    <volume>16</volume>
    <articleNumber>7387</articleNumber>
    <type>article</type>
    <publisherName>Springer Nature</publisherName>
    <publisherPlace>London</publisherPlace>
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    <completedDate>2026-02-23</completedDate>
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    <title language="eng">Quantitative Kernel estimation from traffic signs using slanted edge spatial frequency response as a sharpness metric</title>
    <abstract language="eng">Sharpness is a critical optical property of automotive cameras, measured by the spatial frequency response (SFR) within the end-of-line (EOL) test after manufacturing. This work presents a method to estimate the blurring kernel of an automotive camera, which could be the first step toward state monitoring of automotive cameras. To achieve this, Principal Component Analysis (PCA) was performed, using synthetic kernels generated by Zemax. The PCA model was built with approximately 1300 base kernels representing spatially variant point spread functions (PSFs). This model generates kernel samples during the estimation process. Synthetic images were created by convolving the synthetic kernels with reference traffic sign images and compared with real-life data captured by an automotive camera. These synthetic data were utilized for algorithm development, and later on, validation was performed on real-life data. The algorithm extracts two pixels regions of interest (ROIs) containing slanted edges from the blurred image and crops matching ROIs from a reference sharp image. Each candidate kernel was used to blur the reference ROIs, and the resulting SFR was compared with the blurred ROIs’ SFR. Differential evolution optimization minimizes the SFR difference, selecting the kernel that best matches the observed blur. The final kernel was evaluated against the true kernel for accuracy. The structural similarity index measure (SSIM) between the original and estimated blurred ROIs ranges from 0.808 to 0.945. For true vs. estimated kernels, SSIM varies from 0.92 to 0.98. Pearson correlation coefficients range from 0.84 to 0.99, Cosine similarity from 0.86 to 0.99, and mean squared error (MSE) from to . Validation on real-life camera images showed that the SSIM between the estimated and blurred ROI was &gt;0.82, showing promising accuracy in kernel estimation, which could be used towards in-field monitoring of camera sharpness degradation.</abstract>
    <parentTitle language="eng">Scientific Reports</parentTitle>
    <identifier type="issn">2045-2322</identifier>
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    <author>
      <first_name>Amit</first_name>
      <last_name>Pandey</last_name>
    </author>
    <author>
      <first_name>Zubair Akhtar</first_name>
      <last_name>Mohd</last_name>
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    <author>
      <first_name>Nandana Kappuva</first_name>
      <last_name>Veettil</last_name>
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    <author>
      <first_name>Bernhard</first_name>
      <last_name>Wunderle</last_name>
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      <first_name>Gordon</first_name>
      <last_name>Elger</last_name>
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  <doc>
    <id>6498</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>15</pageNumber>
    <edition/>
    <issue>10</issue>
    <volume>16</volume>
    <articleNumber>1164</articleNumber>
    <type>article</type>
    <publisherName>MDPI</publisherName>
    <publisherPlace>Basel</publisherPlace>
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    <completedDate>2025-12-11</completedDate>
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    <title language="eng">Sintering for High Power Optoelectronic Devices</title>
    <abstract language="eng">Residual-free eutectic Au80Sn20 soldering is still the dominant assembly technology for optoelectronic devices such as high-power lasers, LEDs, and photodiodes. Due to the high cost of gold, alternatives are desirable. This paper investigates the thermal performance of copper-based sintering for optoelectronic submodules on first and second level to obtain thermally efficient thin bondlines. Sintered interconnects obtained by a new particle-free copper ink, based on complexed copper salt, are compared with copper flake and silver nanoparticle sintered interconnects and benchmarked against AuSn solder interconnects. The copper ink is dispensed and predried at 130 °C to facilitate in situ generation of Cu nanoparticles by thermal decomposition of the metal salt before sintering. Submounts are then sintered at 275 °C for 15 min under nitrogen with 30 MPa pressure, forming uniform 2–5 µm copper layers achieving shear strengths above 31 MPa. Unpackaged LEDs are bonded on first level using the copper ink but applying only 10 MPa to avoid damaging the semiconductor dies. Thermal performance is evaluated via transient thermal analysis. Results show that copper ink interfaces approach the performance of thin AuSn joints and match silver interconnects at second level. However, at first level, AuSn and sintered interconnects of commercial silver and copper pastes remained superior due to the relative inhomogeneous thickness of the thin Cu copper layer after predrying, requiring higher bonding pressure to equalize surface inhomogeneities.</abstract>
    <parentTitle language="eng">Micromachines</parentTitle>
    <identifier type="issn">2072-666X</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-64984</identifier>
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    <author>
      <first_name>Hannes</first_name>
      <last_name>Schwan</last_name>
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    <author>
      <first_name>Nihesh</first_name>
      <last_name>Mohan</last_name>
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    <author>
      <first_name>Maximilian</first_name>
      <last_name>Schmid</last_name>
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    <author>
      <first_name>Rocky Kumar</first_name>
      <last_name>Saha</last_name>
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    <author>
      <first_name>Holger</first_name>
      <last_name>Klassen</last_name>
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    <author>
      <first_name>Klaus</first_name>
      <last_name>Müller</last_name>
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    <author>
      <first_name>Gordon</first_name>
      <last_name>Elger</last_name>
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  <doc>
    <id>6487</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
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    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>11</pageNumber>
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    <type>article</type>
    <publisherName>IEEE</publisherName>
    <publisherPlace>New York</publisherPlace>
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    <completedDate>2025-12-08</completedDate>
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    <title language="eng">Comparison of different Input data for the prediction of LED solder joints using Artificial Neural Networks</title>
    <abstract language="eng">Scarcity of raw data is a major issue for applying data driven methods to reliability prediction, so making the best use of what is available is critical. This paper studies how different aspects of measurement data can be used best. Specifically, the reliability of the solder joint of LED packages is predicted based on Transient Thermal Analysis and Scanning Acoustic Microscopy data from a large measurement campaign. The impact of using full temporal information vs measurements at individual datapoints is investigated as well as the benefit of including the SAM data, and different ways of presenting the TTA information, either as a full curve or as expert-selected features. The impact of formatting categorical information of solder and LED package type as one-hot encoding or using embeddings is considered. Finally, the performance for Pass/Fail predictions of the best identified model with a model architecture developed on a similar dataset is compared. We identified the most relevant sources of information for predicting the behavior and the best format for the data, which helps guide the choice for future model architectures.</abstract>
    <parentTitle language="eng">IEEE Transactions on Device and Materials Reliability</parentTitle>
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    <author>
      <first_name>Andreas</first_name>
      <last_name>Zippelius</last_name>
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    <author>
      <first_name>Zubair Akhtar</first_name>
      <last_name>Mohd</last_name>
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    <author>
      <first_name>Maximilian</first_name>
      <last_name>Schmid</last_name>
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      <first_name>Gordon</first_name>
      <last_name>Elger</last_name>
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    <id>6482</id>
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    <publishedYear>2025</publishedYear>
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    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>8</pageNumber>
    <edition/>
    <issue>196</issue>
    <volume>2026</volume>
    <articleNumber>109396</articleNumber>
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    <publisherName>Elsevier</publisherName>
    <publisherPlace>Amsterdam</publisherPlace>
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    <title language="eng">Evaluating optical performance degradation of automotive cameras under accelerated aging</title>
    <abstract language="eng">Automotive cameras are subject to environmental stress, which degrades performance by reducing image sharpness. To qualify for automotive use and to ensure that the cameras maintain sharpness according to the hard requirements of end-of-line testing, cameras have to undergo standardized accelerated aging tests. These tests are performed to demonstrate reliability and functional safety over lifetime. Few studies have been published that demonstrate how aging contributes to the degradation of optical performance. This study addresses this gap by combining accelerated thermal aging with sharpness tracking to investigate degradation over time. To quantify sharpness degradation, six series-production cameras were subjected to accelerated thermal aging between −40◦𝐶 and +85◦𝐶. Each camera underwent 2000 aging cycles, equivalent to 80% of their lifetime based on the Coffin-Manson model of the LV124 standard. Sharpness was measured by calculating the Spatial Frequency Response (SFR) from images captured of a double-cross reticle projected by a virtual object generator with three illumination wavelengths (625nm, 520nm, and 470nm). The change in sharpness was evaluated with SFR50 and SFR at 60 line pairs per millimeter (SFR@60). During the first 250 cycles, a wear-in effect was observed, where sharpness increased before leveling off, as seen previously. The results also indicated a slow decline in sharpness showing long-term stability. Analysis indicated that before aging, the best focal plane was located closer to the focal position of the red wavelength, which lies furthest from the objective. By the end of the aging process, the best focal plane had shifted toward the focal position of the blue wavelength, which is located closer to the objective. This suggests a forward movement of the image sensor due to aging. Even after 2000 cycles, all cameras maintained an SFR@60 above 0.5. A Random Forest regression model was trained to predict the age based on the SFR curves, achieving a mean absolute error of 126 cycles and a 𝑅2 score of 0.96.</abstract>
    <parentTitle language="eng">Optics and Lasers in Engineering</parentTitle>
    <identifier type="issn">1873-0302</identifier>
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    <author>
      <first_name>Amit</first_name>
      <last_name>Pandey</last_name>
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    <author>
      <first_name>Stephan</first_name>
      <last_name>Kühn</last_name>
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    <author>
      <first_name>Alexander</first_name>
      <last_name>Weis</last_name>
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    <author>
      <first_name>Bernhard</first_name>
      <last_name>Wunderle</last_name>
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      <first_name>Gordon</first_name>
      <last_name>Elger</last_name>
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    <completedDate>2025-11-28</completedDate>
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    <title language="eng">Reliability of High-Power LEDs Under Varying Thermal Aging Conditions</title>
    <parentTitle language="eng">2025 31st International Workshop on Thermal Investigations of ICs and Systems (THERMINIC)</parentTitle>
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      <first_name>Elias</first_name>
      <last_name>Hufnagel</last_name>
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      <first_name>Jainam</first_name>
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    <pageLast/>
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    <edition/>
    <issue/>
    <volume>15</volume>
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    <title language="eng">Deep segmentation of 3+1D radar point cloud for real-time roadside traffic user detection</title>
    <abstract language="eng">Smart cities rely on intelligent infrastructure to enhance road safety, optimize traffic flow, and enable vehicle-to-infrastructure (V2I) communication. A key component of such infrastructure is an efficient and real-time perception system that accurately detects diverse traffic participants. Among various sensing modalities, automotive radar is one of the best choices due to its robust performance in adverse weather and low-light conditions. However, due to low spatial resolution, traditional clustering-based approaches for radar object detection often struggle with vulnerable road user detection and nearby object separation. Hence, this paper proposes a deep learning-based D radar point cloud clustering methodology tailored for smart infrastructure-based perception applications. This approach first performs semantic segmentation of the radar point cloud, followed by instance segmentation to generate well-formed clusters with class labels using a deep neural network. It also detects single-point objects that conventional methods often miss. The described approach is developed and experimented using a smart infrastructure-based sensor setup and it performs segmentation of the point cloud in real-time. Experimental results demonstrate 95.35% F1-macro score for semantic segmentation and 91.03% mean average precision (mAP) at an intersection over union (IoU) threshold of 0.5 for instance segmentation. Further, the complete pipeline operates at 43.61 frames per second with a memory requirement of less than 0.7 MB on the edge device (Nvidia Jetson AGX Orin).</abstract>
    <parentTitle language="eng">Scientific Reports</parentTitle>
    <identifier type="issn">2045-2322</identifier>
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    <author>
      <first_name>Savankumar</first_name>
      <last_name>Bhanderi</last_name>
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    <author>
      <first_name>Shiva</first_name>
      <last_name>Agrawal</last_name>
    </author>
    <author>
      <first_name>Gordon</first_name>
      <last_name>Elger</last_name>
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    <pageLast/>
    <pageNumber>8</pageNumber>
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    <publisherName>arXiv</publisherName>
    <publisherPlace>Ithaca</publisherPlace>
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    <completedDate>2025-06-12</completedDate>
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    <title language="eng">Accelerated Real-Life (ARL) Testing and Characterization of Automotive LiDAR Sensors to facilitate the Development and Validation of Enhanced Sensor Models</title>
    <abstract language="eng">In the realm of automated driving simulation and sensor modeling, the need for highly accurate sensor models is paramount for ensuring the reliability and safety of advanced driving assistance systems (ADAS). Hence, numerous works focus on the development of high-fidelity models of ADAS sensors, such as camera, Radar as well as modern LiDAR systems to simulate the sensor behavior in different driving scenarios, even under varying environmental conditions, considering for example adverse weather effects. However, aging effects of sensors, leading to suboptimal system performance, are mostly overlooked by current simulation techniques. This paper introduces a cutting-edge Hardware-in-the-Loop (HiL) test bench designed for the automated, accelerated aging and characterization of Automotive LiDAR sensors. The primary objective of this research is to address the aging effects of LiDAR sensors over the product life cycle, specifically focusing on aspects such as laser beam profile deterioration, output power reduction and intrinsic parameter drift, which are mostly neglected in current sensor models. By that, this proceeding research is intended to path the way, not only towards identifying and modeling respective degradation effects, but also to suggest quantitative model validation metrics.</abstract>
    <identifier type="urn">urn:nbn:de:bvb:573-59856</identifier>
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    <author>
      <first_name>Marcel</first_name>
      <last_name>Kettelgerdes</last_name>
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      <first_name>Tjorven</first_name>
      <last_name>Hillmann</last_name>
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      <first_name>Thomas</first_name>
      <last_name>Hirmer</last_name>
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      <first_name>Hüseyin</first_name>
      <last_name>Erdogan</last_name>
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      <first_name>Bernhard</first_name>
      <last_name>Wunderle</last_name>
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      <first_name>Gordon</first_name>
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    <pageNumber>15</pageNumber>
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    <title language="eng">Quantitative Kernel Estimation from Traffic Signs using Slanted Edge Spatial Frequency Response as a Sharpness Metric</title>
    <abstract language="eng">The sharpness is a critical optical property of automotive cameras, measured by the Spatial Frequency Response (SFR) within the end of line (EOL) test after manufacturing. This work presents a method to estimate the blurring kernel of automotive camera for state monitoring. To achieve this, Principal Component Analysis (PCA) is performed, using synthetic kernels generated by Zemax. The PCA model is built with approximately 1300 base kernels representing spatially variant point spread functions (PSFs). This model generates kernel samples during the estimation process. Synthetic images are created by convolving the synthetic kernels with reference traffic sign images and compared with real-life data captured by an automotive camera. These synthetic data are utilized for algorithm development, and later on validation is performed on real-life data. The algorithm extracts two 45 x 45 pixels regions of interest (ROIs) containing slanted edges from the blurred image and crops matching ROIs from a reference sharp image. Each candidate kernel blurs the reference ROIs, and the resulting Spatial Frequency Response (SFR) is compared with the blurred ROIs’ SFR. Differential evolution optimization minimizes the SFR difference, selecting the kernel that best matches the observed blur. The final kernel is evaluated against the true kernel for accuracy. Structural similarity index measure (SSIM) between the original and estimated blurred ROIs ranges from 0.808 to 0.945. For true vs. estimated kernels, SSIM varies from 0.92 to 0.98. Pearson correlation coefficients range from 0.84 to 0.99, Cosine similarity from 0.86 to 0.98, and mean squared error (MSE) from 1.1 x 10-5 to 8.3 x 10-5. Validation on real-life camera images shows that the SSIM between estimated ROI is 0.82 indicating a sufficient level of accuracy in kernel estimation to detect potential degradation of the camera.</abstract>
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    <author>
      <first_name>Amit</first_name>
      <last_name>Pandey</last_name>
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    <author>
      <first_name>Zubair Akhtar</first_name>
      <last_name>Mohd</last_name>
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    <author>
      <first_name>Nandana Kappuva</first_name>
      <last_name>Veettil</last_name>
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    <author>
      <first_name>Bernhard</first_name>
      <last_name>Wunderle</last_name>
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      <first_name>Gordon</first_name>
      <last_name>Elger</last_name>
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  <doc>
    <id>6199</id>
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    <publishedYear>2025</publishedYear>
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    <pageFirst/>
    <pageLast/>
    <pageNumber>18</pageNumber>
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    <completedDate>2025-09-09</completedDate>
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    <title language="eng">Deep Segmentation of 3+1D Radar Point Cloud for Real-Time Roadside Traffic User Detection</title>
    <abstract language="eng">Smart cities rely on intelligent infrastructure to enhance road safety, optimize traffic flow, and enable vehicle-to-infrastructure (V2I) communication. A key component of such infrastructure is an efficient and real-time perception system that accurately detects diverse traffic participants. Among various sensing modalities, automotive radar is one of the best choices due to its robust performance in adverse weather and low-light conditions. However, due to low spatial resolution, traditional clustering-based approaches for radar object detection often struggle with vulnerable road user detection and nearby object separation. Hence, this paper proposes a deep learning-based 3+1D radar point cloud clustering methodology tailored for smart infrastructure-based perception applications. This approach first performs semantic segmentation of the radar point cloud, followed by instance segmentation to generate well-formed clusters with class labels using a deep neural network. It also detects single-point objects that conventional methods often miss. The described approach is developed and experimented using a smart infrastructure-based sensor setup and it performs segmentation of the point cloud in real-time. Experimental results demonstrate 95.35% F1-macro score for semantic segmentation and 91.03% mean average precision (mAP) at an intersection over union (IoU) threshold of 0.5 for instance segmentation. Further, the complete pipeline operates at 43.61 frames per second with a memory requirement of less than 0.7 MB on the edge device (Nvidia Jetson AGX Orin).</abstract>
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    <author>
      <first_name>Savankumar</first_name>
      <last_name>Bhanderi</last_name>
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      <first_name>Shiva</first_name>
      <last_name>Agrawal</last_name>
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      <first_name>Gordon</first_name>
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    <pageFirst/>
    <pageLast/>
    <pageNumber>15</pageNumber>
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    <volume>15</volume>
    <articleNumber>24340</articleNumber>
    <type>article</type>
    <publisherName>Springer Nature</publisherName>
    <publisherPlace>London</publisherPlace>
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    <completedDate>2025-08-05</completedDate>
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    <title language="eng">AI-driven point cloud framework for predicting solder joint reliability using 3D FEA data</title>
    <abstract language="eng">Crack propagation in solder joints remains a major challenge impacting the thermo-mechanical reliability of electronic devices, underscoring the importance of optimizing package and solder pad designs. Traditional Finite Element Analysis (FEA) techniques for predicting solder joint lifespan often rely on manual post-processing to identify high-risk regions for plastic strain accumulation. However, this manual process can fail to detect complex and subtle failure mechanisms and purely based on averaging the creep strain and correlating it to lifetime values collected from experiments using Coffin Manson equation. To address these limitations, this study presents an Artificial Intelligence (AI) framework designed for automated 3D FEA post-processing of surface-mounted devices (SMDs) assembled to Printed Circuit Board (PCB). This framework integrates 3D Convolutional Neural Networks (CNNs) and PointNet architectures to automatically extract complex spatial features from 3D FEA data. These learned features are then linked to experimentally measured solder joint lifetimes through fully connected neural network layers, allowing the model to capture complex and nonlinear failure behaviours. The research specifically targets crack development in solder joints of ceramic-based high-power LED packages used in automotive lighting systems. This dataset included variations in two-pad and three-pad configurations, as well as thin and thick film metallized ceramic substrates. Results from the study demonstrate that the PointNet model outperforms the 3D CNN, achieving a high correlation with experimental data (R2 = 99.91%). This AI-driven, automated feature extraction approach significantly improves the accuracy and provide the more reliable models for solder joint lifetime predictions, offering a substantial improvement over traditional method.</abstract>
    <parentTitle language="eng">Scientific Reports</parentTitle>
    <identifier type="issn">2045-2322</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-60961</identifier>
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    <author>
      <first_name>Zubair Akhtar</first_name>
      <last_name>Mohd</last_name>
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    <author>
      <first_name>Maximilian</first_name>
      <last_name>Schmid</last_name>
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    <author>
      <first_name>Gordon</first_name>
      <last_name>Elger</last_name>
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  </doc>
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    <id>5942</id>
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    <pageFirst/>
    <pageLast/>
    <pageNumber>26</pageNumber>
    <edition/>
    <issue>11</issue>
    <volume>25</volume>
    <articleNumber>3422</articleNumber>
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    <publisherName>MDPI</publisherName>
    <publisherPlace>Basel</publisherPlace>
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    <title language="eng">Infra-3DRC-FusionNet: Deep Fusion of Roadside Mounted RGB Mono Camera and Three-Dimensional Automotive Radar for Traffic User Detection</title>
    <abstract language="eng">Mono RGB cameras and automotive radar sensors provide a complementary information set that makes them excellent candidates for sensor data fusion to obtain robust traffic user detection. This has been widely used in the vehicle domain and recently introduced in roadside-mounted smart infrastructure-based road user detection. However, the performance of the most commonly used late fusion methods often degrades when the camera fails to detect road users in adverse environmental conditions. The solution is to fuse the data using deep neural networks at the early stage of the fusion pipeline to use the complete data provided by both sensors. Research has been carried out in this area, but is limited to vehicle-based sensor setups. Hence, this work proposes a novel deep neural network to jointly fuse RGB mono-camera images and 3D automotive radar point cloud data to obtain enhanced traffic user detection for the roadside-mounted smart infrastructure setup. Projected radar points are first used to generate anchors in image regions with a high likelihood of road users, including areas not visible to the camera. These anchors guide the prediction of 2D bounding boxes, object categories, and confidence scores. Valid detections are then used to segment radar points by instance, and the results are post-processed to produce final road user detections in the ground plane. The trained model is evaluated for different light and weather conditions using ground truth data from a lidar sensor. It provides a precision of 92%, recall of 78%, and F1-score of 85%. The proposed deep fusion methodology has 33%, 6%, and 21% absolute improvement in precision, recall, and F1-score, respectively, compared to object-level spatial fusion output.</abstract>
    <parentTitle language="eng">Sensors</parentTitle>
    <identifier type="issn">1424-8220</identifier>
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    <author>
      <first_name>Shiva</first_name>
      <last_name>Agrawal</last_name>
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    <author>
      <first_name>Savankumar</first_name>
      <last_name>Bhanderi</last_name>
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    <author>
      <first_name>Gordon</first_name>
      <last_name>Elger</last_name>
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    <pageLast/>
    <pageNumber>7</pageNumber>
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    <publisherName>arXiv</publisherName>
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    <completedDate>2025-04-15</completedDate>
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    <title language="eng">Applying Extended Object Tracking for Self-Localization of Roadside Radar Sensors</title>
    <abstract language="eng">Intelligent Transportation Systems (ITS) can benefit from roadside 4D mmWave radar sensors for large-scale traffic monitoring due to their weatherproof functionality, long sensing range and low manufacturing cost. However, the localization method using external measurement devices has limitations in urban environments. Furthermore, if the sensor mount exhibits changes due to environmental influences, they cannot be corrected when the measurement is performed only during the installation. In this paper, we propose self-localization of roadside radar data using Extended Object Tracking (EOT). The method analyses both the tracked trajectories of the vehicles observed by the sensor and the aerial laser scan of city streets, assigns labels of driving behaviors such as "straight ahead", "left turn", "right turn" to trajectory sections and road segments, and performs Semantic Iterative Closest Points (SICP) algorithm to register the point cloud. The method exploits the result from a down stream task -- object tracking -- for localization. We demonstrate high accuracy in the sub-meter range along with very low orientation error. The method also shows good data efficiency. The evaluation is done in both simulation and real-world tests.</abstract>
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    <author>
      <first_name>Longfei</first_name>
      <last_name>Han</last_name>
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    <author>
      <first_name>Qiuyu</first_name>
      <last_name>Xu</last_name>
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    <author>
      <first_name>Klaus</first_name>
      <last_name>Kefferpütz</last_name>
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    <author>
      <first_name>Gordon</first_name>
      <last_name>Elger</last_name>
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      <first_name>Jürgen</first_name>
      <last_name>Beyerer</last_name>
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    <title language="eng">AI-Driven Point Cloud Framework for Predicting Solder Joint Reliability using 3D FEA Data</title>
    <abstract language="eng">Crack propagation in solder joints remains a critical challenge affecting the thermo-mechanical reliability of electronic devices, emphasizing the need for optimized package and solder pad designs. Traditional Finite Element Analysis (FEA) methods for predicting solder joint lifespan rely heavily on manual post-processing, where high-risk regions for plastic strain accumulation are identified. However, these approaches often overlook intricate failure mechanisms, as they primarily average creep strain and correlate it with experimental lifetime data using the Coffin-Manson equation, limiting their predictive accuracy. &#13;
&#13;
To overcome these limitations, this study introduces a novel AI-driven framework that automates 3D FEA post-processing for surface-mounted devices (SMDs) connected to printed circuit boards (PCBs). Unlike traditional methods, this framework leverages deep learning architectures—specifically, 3D Convolutional Neural Networks (CNNs) and PointNet—to extract complex spatial features directly from 3D FEA data, eliminating the need for manual interpretation. These learned features are then mapped to experimentally measured solder joint lifetimes through fully connected neural network layers, allowing the model to capture nonlinear failure behaviours that conventional methods fail to recognize. &#13;
&#13;
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    <title language="eng">Laser sintering of Cu particle-free inks for high-performance printed electronics</title>
    <abstract language="eng">This study investigates laser sintering of Cu particle-free ink (Cu formate tetrahydrate—amino-2-propanol complex) as an alternative to conventional sintering in an oven (under inert/reducing atmosphere). Utilizing benefits of high-speed localized heating using laser, substrate damage can be prevented for low-melting substrates such as Polyethylene Terephthalate (PET). Firstly, a suitable sintering process window is achieved based on energy density for two different flexible polymeric susbtrates: Polyimide and PET using different laser parameters (laser power, scan rate and spot diameter). Subsequently, characterization of laser sintered traces are also made using different laser optic profiles (Gaussian and top hat). Different methodologies for fabrication of metallized Cu layer were also demonstrated. A very low bulk resistivity of 3.24 µΩcm (1.87 times of bulk Cu) was achieved on trace thickness of 0.85 ± 0.15 µm exhibiting good adherence to polymeric substrates. A promising fabrication process of low-cost and reliable flexible printed electronic devices is demonstrated.</abstract>
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    <title language="eng">Additive Manufacturing for Automotive Radar Sensors Using Copper Inks and Pastes</title>
    <abstract language="eng">Radar sensors are critical for obstacle detection and navigation, especially for automated driving. Using the use-case “printing of heating coils on the inside of the front housing (primary radome)” needed for de-icing in winter, it is demonstrated that additive manufacturing (AM) can provide economic and functional benefits for manufacturing of the sensors. AM will allow significant cost reduction by eliminating parts and simplifying the manufacturing process. Different AM technologies for the coils were investigated, first, by applying the conductive traces by fused deposition modeling (FDM), and, second, by printing copper particle-free inks and pastes. The metal layers were electrically and mechanically characterized using a profilometer to measure the trace dimension and a four-point probe to measure the resistance. It was revealed that low-cost conductive filaments with low resistivity and current carrying capacity are commercially still not available. The best option sourced was a copper–polyester-based filament with 6000 µΩcm after printing. Therefore, low-cost particle-free copper inks and commercial copper flake paste were selected to print the heating coil. The Cu particle-free inks were amine-based Cu (II) formate complexes, where the Cu exists in an ionic form. Using contactless printing processes such as ink-jet printing or pneumatic dispensing, the traces could be deposited onto the low-melting temperature (225 °C) polymeric radome structure. After printing, the material needed to be sintered to form the conductive copper traces. To avoid damaging the polymer radome during sintering, two different processes were investigated: low-temperature (&lt;150 °C) sintering in an oven for 30 min or fast laser sintering. The sintered Cu layers achieved the following specific electric resistivities when slowly sintered in the oven: paste 4 µΩcm and ink 8.8 µΩcm. Using laser sintering, the ink achieved 3.2 µΩcm because the locally high temperature provides better sintering. Also, the adhesion was significantly increased to (5 B). Therefore, laser sintering is the preferred technology. In addition, it allows fast processing directly after printing. Commercial equipment is available where printing and laser sintering is integrated. The potential of low-cost copper material and the integration in additive manufacturing of electronic systems using radar sensors as an example are demonstrated in this paper.</abstract>
    <parentTitle language="eng">Applied Sciences</parentTitle>
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Using the use-case \u201cprinting of heating coils on the inside of the front housing (primary radome)\u201d needed for de-icing in winter, it is demonstrated that additive manufacturing (AM) can provide economic and functional benefits for manufacturing of the sensors. AM will allow significant cost reduction by eliminating parts and simplifying the manufacturing process. Different AM technologies for the coils were investigated, first, by applying the conductive traces by fused deposition modeling (FDM), and, second, by printing copper particle-free inks and pastes. The metal layers were electrically and mechanically characterized using a profilometer to measure the trace dimension and a four-point probe to measure the resistance. It was revealed that low-cost conductive filaments with low resistivity and current carrying capacity are commercially still not available. The best option sourced was a copper\u2013polyester-based filament with 6000 \u00b5\u2126cm after printing. Therefore, low-cost particle-free copper inks and commercial copper flake paste were selected to print the heating coil. The Cu particle-free inks were amine-based Cu (II) formate complexes, where the Cu exists in an ionic form. Using contactless printing processes such as ink-jet printing or pneumatic dispensing, the traces could be deposited onto the low-melting temperature (225 \u00b0C) polymeric radome structure. After printing, the material needed to be sintered to form the conductive copper traces. To avoid damaging the polymer radome during sintering, two different processes were investigated: low-temperature (&amp;lt;150 \u00b0C) sintering in an oven for 30 min or fast laser sintering. The sintered Cu layers achieved the following specific electric resistivities when slowly sintered in the oven: paste 4 \u00b5\u2126cm and ink 8.8 \u00b5\u2126cm. Using laser sintering, the ink achieved 3.2 \u00b5\u2126cm because the locally high temperature provides better sintering. 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    <abstract language="eng">The thermo-mechanical reliability of electronic systems is often limited by the crack growth within the solder joints. Addressing this issue requires careful consideration of the design of the package and solder pads. Finite Element Analysis (FEA) is widely used to predict crack growth and to model their lifetime. Traditionally, FEA post-processing methods rely on human expertise to select appropriate regions for evaluating plastic and creep strain at critical locations and correlating these values with experimental data using the Coffin-Manson equation, which predicts fatigue lifetime based on cyclic plastic strain. This study introduces a novel method for FEA post-processing of surface-mounted devices (SMD) on printed circuit boards (PCB) using artificial intelligence. The method transforms the FEA data into a 2D grid map of creep strain values and employs a Convolutional Neural Network (CNN) for automatic feature extraction. Afterwards, a fully connected layer correlates the extracted features with the experimental measured solder joint lifetime, effectively capturing nonlinear relationships.&#13;
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The study focuses on the development of the concept of crack formation in the solder interconnects of ceramic based high-power LED packages used in the automotive industry for headlights. The validated FEA model is based on an extensive data set of 1800 LED packages including seven different ceramic-based LED packages and five different solders. The design of the ceramic LED package covers two-pad and three-pad footprint for soldering and thin film and thick film metallized ceramic carriers. Results show a strong agreement (R2 Score is 99.867 %) between simulations and experimental data for ceramic LED packages. This automatic feature extraction from FEA data sets a new benchmark for improving solder reliability predictions, and it has proved to be better than established methods for lifetime prediction of solder joints.</abstract>
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    <title language="eng">First steps towards real-world traffic signal control optimisation by reinforcement learning</title>
    <abstract language="eng">Enhancing traffic signal optimisation has the potential to improve urban traffic flow without the need for expensive infrastructure modifications. While reinforcement learning (RL) techniques have demonstrated their effectiveness in simulations, their real-world implementation is still a challenge. Real-world systems need to be developed that guarantee a deployable action definition for real traffic systems while prioritising safety constraints and robust policies. This paper introduces a method to overcome this challenge by introducing a novel action definition that optimises parameter-level control programmes designed by traffic engineers. The complete proposed framework consists of a traffic situation estimation, a feature extractor, and a system that enables training on estimates of real-world traffic situations. Further multimodal optimisation, scalability, and continuous training after deployment could be achieved. The first simulative tests using this action definition show an average improvement of more than 20% in traffic flow compared to the baseline – the corresponding pre-optimised real-world control.</abstract>
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it is performed manually to a large extent. Even though full automation of such a process is difficult, semiautomation can be a significant step to ease this process. However, the available work in this regard is still very limited; hence, in this paper, a novel semi-automatic annotation methodology is developed for labeling RGB camera images and 3D automotive radar point cloud data using a smart infrastructure-based sensor setup. This paper also describes a new method for 3D radar background subtraction to remove clutter and a new object category, GROUP, for radar-based object detection for closely located vulnerable road users. To validate the work, a dataset named INFRA-3DRC is created using this methodology, where 75% of the&#13;
labels are automatically generated. In addition, a radar cluster classifier and an image classifier are developed, trained, and tested on this dataset, achieving accuracy of 98.26% and 94.86%, respectively. The dataset and Python scripts are available at https://fraunhoferivi.github.io/INFRA-3DRC-Dataset/.</abstract>
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      <first_name>Alexey</first_name>
      <last_name>Vinel</last_name>
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      <value>Traffic Flow Optimization</value>
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    <subject>
      <language>eng</language>
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      <value>Transfer Learning</value>
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    <title language="eng">Drop feature optimization for fine trace inkjet printing</title>
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      <first_name>Sri Krishna</first_name>
      <last_name>Bhogaraju</last_name>
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      <first_name>Maximilian</first_name>
      <last_name>Schmid</last_name>
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      <first_name>Hiren R.</first_name>
      <last_name>Kotadia</last_name>
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      <first_name>Fosca</first_name>
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      <first_name>Gordon</first_name>
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      <language>eng</language>
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      <value>etched brass</value>
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      <value>flakes</value>
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      <value>reliability</value>
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    <title language="eng">Multi-Sensor Data Fusion for Real-Time Multi-Object Tracking</title>
    <abstract language="eng">Sensor data fusion is essential for environmental perception within smart traffic applications. By using multiple sensors cooperatively, the accuracy and probability of the perception are increased, which is crucial for critical traffic scenarios or under bad weather conditions. In this paper, a modular real-time capable multi-sensor fusion framework is presented and tested to fuse data on the object list level from distributed automotive sensors (cameras, radar, and LiDAR). The modular multi-sensor fusion architecture receives an object list (untracked objects) from each sensor. The fusion framework combines classical data fusion algorithms, as it contains a coordinate transformation module, an object association module (Hungarian algorithm), an object tracking module (unscented Kalman filter), and a movement compensation module. Due to the modular design, the fusion framework is adaptable and does not rely on the number of sensors or their types. Moreover, the method continues to operate because of this adaptable design in case of an individual sensor failure. This is an essential feature for safety-critical applications. The architecture targets environmental perception in challenging time-critical applications. The developed fusion framework is tested using simulation and public domain experimental data. Using the developed framework, sensor fusion is obtained well below 10 milliseconds of computing time using an AMD Ryzen 7 5800H mobile processor and the Python programming language. Furthermore, the object-level multi-sensor approach enables the detection of changes in the extrinsic calibration of the sensors and potential sensor failures. A concept was developed to use the multi-sensor framework to identify sensor malfunctions. This feature will become extremely important in ensuring the functional safety of the sensors for autonomous driving.</abstract>
    <parentTitle language="eng">Processes</parentTitle>
    <identifier type="issn">2227-9717</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-31989</identifier>
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    <author>
      <first_name>Numan</first_name>
      <last_name>Senel</last_name>
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    <author>
      <first_name>Klaus</first_name>
      <last_name>Kefferpütz</last_name>
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    <author>
      <first_name>Kristina</first_name>
      <last_name>Doycheva</last_name>
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    <author>
      <first_name>Gordon</first_name>
      <last_name>Elger</last_name>
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    <subject>
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      <value>unscented Kalman filter</value>
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