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A Multi-Modal Light Sheet Microscope for High-Resolution 3D Tomographic Imaging with Enhanced Raman Scattering and Computational Denoising
Pooja Kumari,
Björn Van Marwick,
Johann Kern,
Matthias Rädle
Three-dimensional (3D) cellular models, such as spheroids, serve as pivotal systems for understanding complex biological phenomena in histology, oncology, and tissue engineering. In response to the growing need for advanced imaging capabilities, we present a novel Multi-Modal Raman Light Sheet Microscope designed to capture elastic (Rayleigh) and inelastic (Raman) scattering, along with fluorescence signals, in a single platform. By leveraging a shorter excitation wavelength (532nm) to boost Raman scattering efficiency and incorporating robust fluorescence suppression, the system achieves label-free, high-resolution tomographic imaging without the drawbacks commonly associated with near-infrared modalities. An accompanying Deep Image Prior (DIP) seamlessly integrates with the microscope to provide unsupervised denoising and resolution enhancement, preserving critical molecular details and minimizing extraneous artifacts. Altogether, this synergy of optical and computational strategies underscores the potential for in-depth, 3D imaging of biomolecular and structural features in complex specimens and sets the stage for future advancements in biomedical research, diagnostics, and therapeutics.
Three-dimensional (3D) cellular models, such as spheroids, serve as pivotal systems for understanding complex biological phenomena in histology, oncology, and tissue engineering. In response to the growing need for advanced imaging capabilities, we present a novel Multi-Modal Raman Light Sheet Microscope designed to capture elastic (Rayleigh) and inelastic (Raman) scattering, along with fluorescence signals, in a single platform. By leveraging a shorter excitation wavelength (532nm) to boost Raman scattering efficiency and incorporating robust fluorescence suppression, the system achieves label-free, high-resolution tomographic imaging without the drawbacks commonly associated with near-infrared modalities. An accompanying Deep Image Prior (DIP) seamlessly integrates with the microscope to provide unsupervised denoising and resolution enhancement, preserving critical molecular details and minimizing extraneous artifacts. Altogether, this synergy of optical and computational strategies underscores the potential for in-depth, 3D imaging of biomolecular and structural features in complex specimens and sets the stage for future advancements in biomedical research, diagnostics, and therapeutics.
Posted: 17 February 2025
Kac-Moody Quaternion Lie Algebra
Ferdi Ferdi,
Amir Kamal Amir,
Andi Muhammad Anwar
Posted: 17 February 2025
Linearized Expressions of 3D Rotational Motion
Yinlong Liu
Posted: 17 February 2025
Principal Component Random Forest for Passenger Demand Forecasting in Cooperative, Connected, and Automated Mobility
Georgios Spanos,
Antonios Lalas,
Konstantinos Votis,
Dimitrios Tzovaras
Posted: 17 February 2025
A Multi-Head Attention-Based Transformer Model for Predicting Causes in Aviation Incident
Aziida Nanyonga,
Hassan Wasswa,
Keith Joiner,
Ugur Turhan,
Graham Wild
The timely identification of probable causes in aviation incidents is crucial for averting future tragedies and safeguarding passengers. Typically, investigators rely on flight data recorders, yet delays in data retrieval or damage to the devices can impede progress. In such instances, experts resort to supplementary sources like eyewitness tes- timonies and radar data to construct analytical narratives. Delays in this process have tangible consequences, as evidenced by the Boeing 737 MAX accidents involving Lion Air and Ethiopian Airlines, where the same design flaw resulted in catastrophic outcomes. To streamline investigations, scholars advocate for natural language processing (NLP) and topic modeling methodologies, which organize pertinent aviation terms for rapid analysis. However, existing techniques lack a direct mechanism for deducing probable causes. Bridging this gap, this study proposed a transformer-based model for predict- ing likely causes from raw text narrative inputs, leveraging advancements in long-input transformers. By training the model on comprehensive aviation incident investigation reports like those from the National Transportation Safety Board (NTSB), the proposed approach exhibits promising performance across key evaluation metrics, including Bilin- gual Evaluation Understudy (BLEU) with (M=0.727, SD=0.33), Latent Semantic Analysis (LSA similarity) with (M=0.696, SD=0.152), and Recall Oriented Understudy for Gisting Evaluation (ROUGE) with a precision, recall and F-measure scores of (M=0.666, SD=0.217), (M=0.610, SD=0.211), (M=0.618, SD=0.192) for rouge-1, (M=0.488, SD=0.264), (M=0.448, SD=0.257), M=0.452, SD=0.248) for rouge-2 and (M=0.602, SD=0.241), (M=0.553, SD=0.235), (M=0.5560, SD=0.220) for rouge-L, respectively. This demonstrates its potential to expedite investigations by promptly identifying probable causes from analysis narratives, thus bolstering aviation safety protocols
The timely identification of probable causes in aviation incidents is crucial for averting future tragedies and safeguarding passengers. Typically, investigators rely on flight data recorders, yet delays in data retrieval or damage to the devices can impede progress. In such instances, experts resort to supplementary sources like eyewitness tes- timonies and radar data to construct analytical narratives. Delays in this process have tangible consequences, as evidenced by the Boeing 737 MAX accidents involving Lion Air and Ethiopian Airlines, where the same design flaw resulted in catastrophic outcomes. To streamline investigations, scholars advocate for natural language processing (NLP) and topic modeling methodologies, which organize pertinent aviation terms for rapid analysis. However, existing techniques lack a direct mechanism for deducing probable causes. Bridging this gap, this study proposed a transformer-based model for predict- ing likely causes from raw text narrative inputs, leveraging advancements in long-input transformers. By training the model on comprehensive aviation incident investigation reports like those from the National Transportation Safety Board (NTSB), the proposed approach exhibits promising performance across key evaluation metrics, including Bilin- gual Evaluation Understudy (BLEU) with (M=0.727, SD=0.33), Latent Semantic Analysis (LSA similarity) with (M=0.696, SD=0.152), and Recall Oriented Understudy for Gisting Evaluation (ROUGE) with a precision, recall and F-measure scores of (M=0.666, SD=0.217), (M=0.610, SD=0.211), (M=0.618, SD=0.192) for rouge-1, (M=0.488, SD=0.264), (M=0.448, SD=0.257), M=0.452, SD=0.248) for rouge-2 and (M=0.602, SD=0.241), (M=0.553, SD=0.235), (M=0.5560, SD=0.220) for rouge-L, respectively. This demonstrates its potential to expedite investigations by promptly identifying probable causes from analysis narratives, thus bolstering aviation safety protocols
Posted: 17 February 2025
Deep Supervised Attention Network for Dynamic Scene Deblurring
Seok-Woo Jang,
Limin Yan,
Gye-Young Kim
Posted: 17 February 2025
Role of Secure Boot in Protecting UEFI Capsule Updates
Godwin Olaoye
Posted: 17 February 2025
Exploring Diverse AI Models for Enhanced Land Use and Land Cover Classification in the Nile Delta, Egypt Using Sentinel-Based Data
Mona Maze,
Samar Attaher,
Mohamed O. Taqi,
Rania Elsawy,
Manal M.H. Gad El-Moula,
Fadl A. Hashem,
Ahmed S. Moussa
This study investigated Land Use and Land Cover (LULC) classification east of the Nile Delta, Egypt, using Sentinel-2 bands, spectral indices, and Sentinel-1 data. The aim was to enhance agricultural planning and decision-making by providing timely and accurate information, addressing limitations of manual data collection. Several Machine Learning (ML) and Deep Learning (DL) models were trained and tested using distinct temporal datasets to ensure model independence. Ground truth annotations, validated against a reference Google satellite map, supported training and evaluation. XGBoost achieved the highest overall accuracy (94.4%), surpassing the Support Vector Classifier (84.3%), while Random Forest produced the most accurate map with independent data. Combining Sentinel-1 and Sentinel-2 data improved accuracy by approximately 10%. Strong performance was observed across Recall, Precision, and F1-Score metrics, particularly for urban and aquaculture classes. Uniform Manifold Approximation and Projection (UMAP) technique effectively visualized data distribution, though complete class separation was not achieved. Despite their small size, road area predictions were reliable. This research highlights the potential of integrating multi-sensor data with advanced algorithms for improved LULC classification and emphasizes the need for enhanced ground truth data in future studies.
This study investigated Land Use and Land Cover (LULC) classification east of the Nile Delta, Egypt, using Sentinel-2 bands, spectral indices, and Sentinel-1 data. The aim was to enhance agricultural planning and decision-making by providing timely and accurate information, addressing limitations of manual data collection. Several Machine Learning (ML) and Deep Learning (DL) models were trained and tested using distinct temporal datasets to ensure model independence. Ground truth annotations, validated against a reference Google satellite map, supported training and evaluation. XGBoost achieved the highest overall accuracy (94.4%), surpassing the Support Vector Classifier (84.3%), while Random Forest produced the most accurate map with independent data. Combining Sentinel-1 and Sentinel-2 data improved accuracy by approximately 10%. Strong performance was observed across Recall, Precision, and F1-Score metrics, particularly for urban and aquaculture classes. Uniform Manifold Approximation and Projection (UMAP) technique effectively visualized data distribution, though complete class separation was not achieved. Despite their small size, road area predictions were reliable. This research highlights the potential of integrating multi-sensor data with advanced algorithms for improved LULC classification and emphasizes the need for enhanced ground truth data in future studies.
Posted: 17 February 2025
Defining a Satisfying Expected Value From Chosen Sequences of Bounded Functions Converging to Pathological Functions
Bharath Krishnan
Posted: 17 February 2025
Image Deconvolution To Resolve Astronomical X-ray Sources in Close Proximity: The NuSTAR Images of SXP 15.3 and SXP 305
Sayantan Bhattacharya,
Dimitris M. Christodoulou,
Silas G. T. Laycock
Posted: 17 February 2025
Understanding UEFI Capsule Update Mechanisms
Sheed Iseal
Posted: 17 February 2025
Mitigating Man-in-the-Middle Attacks in UEFI Capsule Updates
Godwin Olaoye
Unified Extensible Firmware Interface (UEFI) is a critical component in the boot process of modern computing systems, responsible for initializing hardware and loading the operating system. UEFI firmware updates, typically delivered in the form of "capsules," are essential for ensuring that a system operates with the latest security patches, bug fixes, and feature enhancements. However, as these updates are often transmitted over untrusted communication channels, they are vulnerable to Man-in-the-Middle (MitM) attacks. In such attacks, a malicious actor can intercept, modify, or inject harmful code into the firmware update, potentially compromising system security and integrity.This paper investigates the potential security risks associated with MitM attacks on UEFI capsule updates and proposes a set of robust countermeasures. We begin by analyzing the typical attack vectors, focusing on the vulnerabilities inherent in the process of transmitting update data between the system and update servers. Drawing from existing cryptographic protocols, we propose the use of strong encryption, digital signatures, and public-key infrastructures (PKIs) to ensure the authenticity, confidentiality, and integrity of UEFI capsule updates. These measures help verify that updates have not been tampered with during transit, preventing unauthorized code injection by attackers.Furthermore, we explore the role of Secure Boot and Trusted Platform Module (TPM) technologies in providing an additional layer of defense. By using secure boot processes, systems can verify that only authorized firmware updates are executed, thus preventing malicious code from being activated. Similarly, TPM can be employed to securely store cryptographic keys and validation certificates, reducing the risk of key compromise during the update process.We also delve into the concept of certificate pinning, where specific trusted certificate authorities are hardcoded into the UEFI firmware, making it more difficult for attackers to present fraudulent certificates during the update process. This approach significantly reduces the likelihood of MitM attacks exploiting misconfigured or compromised certificate chains.
Unified Extensible Firmware Interface (UEFI) is a critical component in the boot process of modern computing systems, responsible for initializing hardware and loading the operating system. UEFI firmware updates, typically delivered in the form of "capsules," are essential for ensuring that a system operates with the latest security patches, bug fixes, and feature enhancements. However, as these updates are often transmitted over untrusted communication channels, they are vulnerable to Man-in-the-Middle (MitM) attacks. In such attacks, a malicious actor can intercept, modify, or inject harmful code into the firmware update, potentially compromising system security and integrity.This paper investigates the potential security risks associated with MitM attacks on UEFI capsule updates and proposes a set of robust countermeasures. We begin by analyzing the typical attack vectors, focusing on the vulnerabilities inherent in the process of transmitting update data between the system and update servers. Drawing from existing cryptographic protocols, we propose the use of strong encryption, digital signatures, and public-key infrastructures (PKIs) to ensure the authenticity, confidentiality, and integrity of UEFI capsule updates. These measures help verify that updates have not been tampered with during transit, preventing unauthorized code injection by attackers.Furthermore, we explore the role of Secure Boot and Trusted Platform Module (TPM) technologies in providing an additional layer of defense. By using secure boot processes, systems can verify that only authorized firmware updates are executed, thus preventing malicious code from being activated. Similarly, TPM can be employed to securely store cryptographic keys and validation certificates, reducing the risk of key compromise during the update process.We also delve into the concept of certificate pinning, where specific trusted certificate authorities are hardcoded into the UEFI firmware, making it more difficult for attackers to present fraudulent certificates during the update process. This approach significantly reduces the likelihood of MitM attacks exploiting misconfigured or compromised certificate chains.
Posted: 17 February 2025
A New Proof Of Eckart-Young-Mirsky Theorem
Haoyuan Wang
Posted: 17 February 2025
Adaptive, ML-Enhanced Resource Management for High-Performance Cloud-Based Web Platforms
Hojoon Lee,
Minjun Kim
Posted: 17 February 2025
Can AI ever become conscious?
Ashkan Farhadi
Posted: 17 February 2025
Development of surveillance robot based on Face Recognition using High Order Statistical Features and Evidence Theory
Rafika Harrabi,
Slim Ben Chaabane,
Hassene Seddik
The recent advancements in technologies such as artificial intelligence (AI), computer vision (CV), and Internet of Things (IoT) have significantly extended various fields, particularly in surveillance systems. These innovations enable real-time facial recognition processing, enhancing security and ensuring safety. However, mobile robots are commonly employed in surveillance systems to handle risky tasks that are beyond human capability. In this context, we present a prototype of a cost-effective mobile surveillance robot built on the Raspberry PI 4, designed for integration into various industrial environments. This smart robot detects intruders using IoT and face recognition technology. The proposed system is equipped with passive infrared (PIR) sensor and a camera for capturing live-streaming video and photos, which are sent to the control room through IoT technology. Additionally, the system uses the face recognition algorithms to differentiate between company staff and potential intruders. The face recognition method combines high-order statistical features and evidence theory to improve facial recognition accuracy and robustness. High-order statistical features are used to capture complex patterns in facial images, enhancing discrimination between individuals. Evidence theory is employed to integrate multiple information sources, allowing for better decision-making under uncertainty. This approach effectively addresses challenges such as variations in lighting, facial expressions, and occlusions, resulting in a more reliable and accurate face recognition system. Upon detecting an unfamiliar individual, the system sends alert notifications and an email with the captured image to the control room through IoT. Additionally, a web interface was created to remotely operate the robot via a WiFi connection. The proposed method for human face recognition is evaluated, and a comparative analysis with existing techniques is conducted. Experimental results with 400 test images of 40 individuals demonstrates the effectiveness of combining various attribute images in improving human face recognition performance. Experimental results shows that the algorithm achieves an accuracy of 98.63% in identifying human faces.
The recent advancements in technologies such as artificial intelligence (AI), computer vision (CV), and Internet of Things (IoT) have significantly extended various fields, particularly in surveillance systems. These innovations enable real-time facial recognition processing, enhancing security and ensuring safety. However, mobile robots are commonly employed in surveillance systems to handle risky tasks that are beyond human capability. In this context, we present a prototype of a cost-effective mobile surveillance robot built on the Raspberry PI 4, designed for integration into various industrial environments. This smart robot detects intruders using IoT and face recognition technology. The proposed system is equipped with passive infrared (PIR) sensor and a camera for capturing live-streaming video and photos, which are sent to the control room through IoT technology. Additionally, the system uses the face recognition algorithms to differentiate between company staff and potential intruders. The face recognition method combines high-order statistical features and evidence theory to improve facial recognition accuracy and robustness. High-order statistical features are used to capture complex patterns in facial images, enhancing discrimination between individuals. Evidence theory is employed to integrate multiple information sources, allowing for better decision-making under uncertainty. This approach effectively addresses challenges such as variations in lighting, facial expressions, and occlusions, resulting in a more reliable and accurate face recognition system. Upon detecting an unfamiliar individual, the system sends alert notifications and an email with the captured image to the control room through IoT. Additionally, a web interface was created to remotely operate the robot via a WiFi connection. The proposed method for human face recognition is evaluated, and a comparative analysis with existing techniques is conducted. Experimental results with 400 test images of 40 individuals demonstrates the effectiveness of combining various attribute images in improving human face recognition performance. Experimental results shows that the algorithm achieves an accuracy of 98.63% in identifying human faces.
Posted: 17 February 2025
A Comprehensive Literature Review on the Use of Restricted Boltzmann Machines and Deep Belief Networks for Human Action Recognition
Majid Joudaki
This literature review provides a comprehensive synthesis of research on the use of Restricted Boltzmann Machines (RBMs) and Deep Belief Networks (DBNs) for human action recognition (HAR) from 2012 to the present. The review begins by introducing the theoretical foundations of RBMs and DBNs, detailing their architectures, training algorithms (notably contrastive divergence), and various extensions—including convolutional and recurrent adaptations—that have been developed to better capture the spatial–temporal dynamics inherent in video data. Key contributions in the field are systematically analyzed, with emphasis on hybrid models that integrate RBM/DBN pretraining with modern deep learning techniques to enhance feature extraction and improve recognition accuracy. The review also examines the major benchmark datasets used in HAR research (such as KTH, HMDB51, UCF101, NTU RGB+D, and Kinetics), discussing preprocessing strategies, evaluation metrics, and the challenges associated with overfitting, computational complexity, and model interpretability. In addition, recent trends such as the incorporation of attention mechanisms, self-supervised learning, and multi-modal data fusion are explored. By highlighting both the historical significance and the evolving advancements of RBM/DBN methodologies, this review provides insights into the current state of HAR research and outlines promising directions for future investigation, including the integration of generative pretraining with emerging architectures for robust and efficient real-time action recognition.
This literature review provides a comprehensive synthesis of research on the use of Restricted Boltzmann Machines (RBMs) and Deep Belief Networks (DBNs) for human action recognition (HAR) from 2012 to the present. The review begins by introducing the theoretical foundations of RBMs and DBNs, detailing their architectures, training algorithms (notably contrastive divergence), and various extensions—including convolutional and recurrent adaptations—that have been developed to better capture the spatial–temporal dynamics inherent in video data. Key contributions in the field are systematically analyzed, with emphasis on hybrid models that integrate RBM/DBN pretraining with modern deep learning techniques to enhance feature extraction and improve recognition accuracy. The review also examines the major benchmark datasets used in HAR research (such as KTH, HMDB51, UCF101, NTU RGB+D, and Kinetics), discussing preprocessing strategies, evaluation metrics, and the challenges associated with overfitting, computational complexity, and model interpretability. In addition, recent trends such as the incorporation of attention mechanisms, self-supervised learning, and multi-modal data fusion are explored. By highlighting both the historical significance and the evolving advancements of RBM/DBN methodologies, this review provides insights into the current state of HAR research and outlines promising directions for future investigation, including the integration of generative pretraining with emerging architectures for robust and efficient real-time action recognition.
Posted: 16 February 2025
Research and Design of Three-Joint Bionic Fish Based on BCF Model
Bojian Yu,
Petro Pavlenko
Posted: 14 February 2025
Using AI-Based Approaches to Sustainably Develop Energy Production in a Solar Power Plant
Farhad Khosrojerdi,
Stéphane Gagnon,
Raul Valverde
Posted: 14 February 2025
A Novel Machine Vision-Based Collision Risk Warning Method for Unsignalized Intersections on Arterial Roads
Zhongbin Luo,
Yanqiu Bi,
Qing Ye,
Yong Li,
Shaofei Wang
Posted: 14 February 2025
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