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Energy Management System for a Smart Green Nanogrid feeding a Research Laboratory with Autonomous Mobile Robots

IFAC-PapersOnLine 2024年第2期58卷 156-161页

作者： Matteo Fresia Lorenzo De Simone Ilaria Traverso Stefano Bracco Silvia Siri Department of Electrical Electronic Telecommunication Engineering and Naval Architecture University of Genoa 16145 Genova Italy Department of Computer Science Bioengineering Robotics and Systems Engineering University of Genoa 16145 Genova Italy

This paper proposes a mixed-integer linear programming optimization model used to define an energy management system tailored for nanogrids in buildings, integrating renewable energy sources, battery energy storage systems and task-executing autonomous mobile robots. Focused on a nanogrid to be realised at the Savona Campus of the University of Genoa, the energy management system optimizes power flows and robot task scheduling in order to minimize the operating costs, the curtailment of the photovoltaic source and the number of unperformed tasks. Its novelty lies in combining energy and task planning constraints, offering significant potential for sustainable building energy management.

关键词： Energy management system Optimal operation Nanogrids Prosumer building Autonomous mobile robots Renewable energy sources Battery energy storage system

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ISGM-DR: Important Slice-Guided Model for Diabetic Retinopathy Diagnosis Using 3D Oct Images

ISGM-DR: Important Slice-Guided Model for Diabetic Retinopat...

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IEEE International Symposium on Biomedical Imaging

作者： Mohamed Elsharkawy Ibrahim Abdelhalim Mohammed Ghazal Ali Mahmoud Harpal S. Sandhu Ayman El-Baz Department of Bioengineering University of Louisville Louisville KY USA Electrical and Computer Engineering Department Abu Dhabi University Abu Dhabi UAE

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Early detection of diabetic retinopathy (DR) is essential for preventing irreversible vision loss. This study introduces the Important Slice-Guided Model for DR Diagnosis (ISGM-DR), which utilizes 3D-Optical Coherence Tomography (OCT) images to effectively differentiate between normal retinas and those affected by DR. As the name suggests, we propose that within the 3D OCT images lies a critical slice, guiding ophthalmologists to swift diagnosis and action upon detecting abnormalities. Accordingly, our model is composed of two key components: the Adaptive Importance Slice-based Network (AISN) and the Channel-Aware Information Slice Block (CAISB). The AISN generates a probability map for each slice, quantifying the importance of each slice in contributing to the diagnosis of DR. Based on these probability maps, we identify the most informative slice from the 3D OCT images. Furthermore, to extract additional evidence regarding the semantic cues associated with DR, we apply the CAISB to obtain highly attended slices enriched with information pertinent to the disease. To propagate this information to the output of the backbone network, we perform element-wise multiplication on the feature maps of the backbone and the highly informative slice. This process guides the entire model toward accurate DR diagnosis. Evaluating a diverse cohort of 481 3D-OCT volumes from both healthy and diseased subjects, our ISGM-DR model achieves an impressive overall accuracy of 93.78%, with a recall of 85.71% and a precision of 92.31%. This performance significantly surpasses existing state-of-the-art methods for DR classification, including Vision Transformer (ViT), Max ViT, and other baseline CNN models.

关键词： Solid modeling Diabetic retinopathy Three-dimensional displays Accuracy Biological system modeling Semantics Tomography Transformers Retina Diseases

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Histopathological Diagnosis of Meningioma and Solitary Fibrous Tumors Based on a Multi-scale Fusion Approach Utilizing Vision Transformer and Texture Analysis 27th

Histopathological Diagnosis of Meningioma and Solitary Fib...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Azam, Mohamed T. Balaha, Hossam Magdy Gondim, Dibson D. Mistry, Akshitkumar Ghazal, Mohammed El-Baz, Ayman Bioengineering Department University of Louisville LouisvilleKY United States Department of Pathology University of Louisville School of Medicine LouisvilleKY United States Department of Neurosurgery University of Louisville School of Medicine LouisvilleKY United States Electrical Computer and Biomedical Engineering Department Abu Dhabi University Abu Dhabi United Arab Emirates

ISBN: (纸本)9783031781032

Integration of Vision Transformer models with texture analysis presents a novel dual-stage approach for histopathology diagnosis. The proposed approach is mainly focused on discriminating between Meningioma (MEN) and Solitary Fibrous Tumor (SFT), two tumors known for their similar morphological characteristics. This approach leverages the inherent power of ViT models to capture global and local features from whole-slide images (WSIs), complementing this capability by integrating texture analysis techniques aimed at improving classification accuracy. Initially, ViT models are applied across three levels of WSI magnification, using rotationally and multi-scaled tiles to handle diverse scales and orientations inherent in histopathological imagery. ViT’s attention mechanisms capture intricate details and spatial correlations within WSIs, offering a comprehensive view of histological structures. Concurrently, texture analysis methods including 3D-CLBP, 3D-GLCM, and 3D-GLRLM are used to extract the inherent patterns of the WSIs, such as homogeneity/inhomogeneity, morphology, and connectivity alongside the three RGB channels to capture the influence of color features. The scores obtained at the output of both stages are then fused and passed to a deep neural network, enabling a more reliable diagnosis. The experimental results show an accuracy of 93.42%, sensitivity of 92.15%, specificity of 94.73%, precision of 94.74%, balanced accuracy of 93.44%, and F1 score of 93.42%. These results elucidate the potential of the proposed approach in enhancing histopathology diagnostics. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep neural networks

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Prediction of IUGR condition at birth by means of CTG recordings and a ResNet model

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computers in Biology and Medicine 2025年 190卷 110123-110123页

作者： Spairani, Edoardo Steyde, Giulio Spuri Forotti, Federica Magenes, Giovanni Signorini, Maria G. Department of Electrical Computer and Biomedical Engineering Università di Pavia Pavia27100 Italy Department of Electronics Information and Bioengineering Politecnico di Milano Milano20133 Italy

Objective: Sub-optimal uterine-placental perfusion and fetal nutrition can lead to intrauterine growth restriction (IUGR), also called fetal growth restriction (FGR). Antenatal cardiotocography (CTG) can aid in the early detection of IUGR. Reliably diagnosing IUGR before delivery remains challenging, and deep learning (DL) techniques offer potential solutions. This paper describes the development of a DL approach to predict an IUGR condition at birth by using CTG signals collected during antenatal monitoring. Materials and methods: Our method is encapsulated in the concept of a two-step training process of a ResNet architecture. The primary focus is on the minimization of data loss, which motivates the division into "presumed" and "confirmed" datasets, which is employed to distinguish based on the presence of information at birth. The method involves fine-tuning: the initial training utilizes "presumed" data to train the network, and the subsequent training employs data representing certain knowledge to refine its performance. Results: The DL model reaches a balanced accuracy of 80% on a hold-out test set of confirmed cases, which is better than what obtained by using standard clinical guidelines. Discussion: The results of our work are compared to the results of similar papers dealing with the prediction of IUGR condition at birth and in general with the prediction of fetal pathological conditions. Our final results are obtained using a very large dataset compared to other papers reported in the literature. Conclusion: The inclusion of DL methods on CTG signals may complement imaging technologies and improve the early detection of IUGR. © 2025 The Authors

关键词： Neonatal monitoring

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Estimating and Analyzing Neural Information flow using Signal Processing on Graphs

Estimating and Analyzing Neural Information flow using Signa...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Felix Schwock Julien Bloch Les Atlas Shima Abadi Azadeh Yazdan-Shahmorad Department of Electrical and Computer Engineering University of Washington Seattle Department of Bioengineering University of Washington Seattle

Correlating neural communication in brain networks with behavior and cognition can provide fundamental insights into the functionality of both healthy and diseased brains. We demonstrate how communication in the brain can be estimated from recorded neural activity using concepts from graph signal processing. The communication is modeled as a flow signals on the edges of a graph and naturally arises from a graph diffusion process. We apply the diffusion model to micro-electrocorticography (ECoG) recordings from sensorimo-tor cortex of two non-human primates to estimate the neural communication flow during excitatory optogenetics. Comparisons with a baseline model demonstrate that adding the neural flow can improve ECoG predictions. Finally, we demonstrate how the neural flow can be decomposed into a gradient and rotational component and show that the gradient component depends on the location of stimulation. This technique, for the first time, offers the opportunity to study neural communication on an unprecedented spatiotemporal scale.

关键词： Neuroscience Neural circuits Neural activity Diffusion processes Predictive models Brain modeling Optogenetics

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Remarks on the Gradient Training of Linear Neural Network Based Feedback for the LQR Problem

Remarks on the Gradient Training of Linear Neural Network Ba...

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IEEE Conference on Decision and Control

作者： Arthur Castello B. De Oliveira Milad Siami Eduardo D. Sontag Department of Electrical & Computer Engineering Northeastern University Boston MA USA Department of Bioengineering Northeastern University Boston MA USA

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

Motivated by the growing use of Artificial intelligence (AI) tools in control design, this paper takes steps toward bridging results from Direct Gradient methods for the Linear Quadratic Regulator (LQR), and neural networks. More specifically, it looks into the case where one wants to find a Linear Feed-Forward Neural Network (LFFNN) feedback that minimizes the LQR cost. This work develops gradient formulas that can be used to implement the training of such networks and derives an important conservation law of the system. This conservation law is then leveraged to prove the global convergence of solutions and invariance of the set of stabilizing networks under the training dynamics. These theoretical results are followed by an extensive analysis of the simplest version of the problem (the “scalar case”) and by numerical evidence of faster convergence of the training of general LFFNNs when compared to traditional direct gradient methods.

关键词： Training Gradient methods Costs Regulators Control design Linear regression Feedforward neural networks Artificial intelligence Optimization Convergence

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Galvanic Body-Coupled Powering for Wireless Implanted Neurostimulators

arXiv

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arXiv 2024年

作者： Omi, Asif Iftekhar Farina, Emma Jiang, Anyu Khalifa, Adam Srinivasan, Shriya Chatterjee, Baibhab Department of Electrical and Computer Engineering University of Florida Gainesville United States Department of Bioengineering Harvard University Boston United States

Body-coupled powering (BCP) is an innovative wireless power transfer (WPT) technique, recently explored for its potential to deliver power to cutting-edge biomedical implants such as nerve/muscle stimulators. This paper demonstrates the efficient technique of designing WPT systems embedding BCP via galvanic coupling (G-BCP). The G-BCP configuration utilizes two metal circular rings surrounding the body area of interest as the transmitter (TX) electrodes required for galvanic (differential) excitation and a wireless implant as the receiver (RX) equipped with two electrodes for differential power reception accordingly. By focusing on the unique advantages of this approach-such as enhanced targeting accuracy, improved power transfer efficiency (PTE), and favorable tissue penetration characteristics, G-BCP emerges as a superior alternative to traditional WPT methods. A comprehensive analysis is conducted to obtain the optimized device parameters while simultaneously allowing flexible placement of implants at different depths and alignments. To substantiate the proposed design concept, a prototype was simulated in Ansys HFSS, employing a multi-layered tissue medium of 10mm radius and targeting the sciatic nerve of a rat. Impressively, this prototype achieves > 20% PTE at 1.25 GHz, with the implant (radius of RX electrodes = 1 mm) located 2 mm deep inside the tissue model having complex load impedance of Rload = 1000Ω and Cload = 5pF. Therefore, the G-BCPbased wirelessly powered microdevices are envisaged to be a key enabler in neural recording and stimulation, specifically for the peripheral nervous system, enhancing therapeutic outcomes and patient experiences. Copyright © 2024, The Authors. All rights reserved.

关键词： Inductive power transmission

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Real-Time Privacy-Preserving Fall Risk Assessment with a Single Body-Worn Tracking Camera

Real-Time Privacy-Preserving Fall Risk Assessment with a Sin...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Chiao-Yi Wang Faranguisse Kakhi Sadrieh Yi-Ting Shen Giovanni Oppizzi Li-Qun Zhang Yang Tao Department of Bioengineering University of Maryland College Park USA Department of Electrical and Computer Engineering University of Maryland College Park USA

Falls are a major cause of injury among elderly adults. In addition, recent research indicates that elderly adults often fall because of the same individual biomechanical causes. Therefore, real-time individual fall risk assessment is crucial for designing a more effective and personalized fall reduction training program. However, existing methods for fall risk assessment either raise privacy concerns or require multiple wearable sensors. In this paper, we propose EgoFall, a real-time privacy-preserving fall risk assessment system using a commercial tracking camera. EgoFall utilizes a chest-mounted tracking camera and a carefully designed data pre-processing pipeline to acquire the ego-body motion data of the subject. The ego-body motion data is then fed to a lightweight CNN-Transformer model for fall risk assessment. We demonstrate that EgoFall not only outperforms the baseline methods but also has very low computational complexity, which is highly suitable for real-time processing.

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An Integrated Computational Framework for the Neurobiology of Memory Based on the ACT-R Declarative Memory System

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Computational Brain and Behavior 2024年第1期7卷 129-149页

作者： Stocco, Andrea Rice, Patrick Thomson, Robert Smith, Briana Morrison, Don Lebiere, Christian Department of Psychology University of Washington Seattle WA United States Department of Psychology Rice University Houston TX United States Department of Electrical Engineering & Computer Science United States Military Academy West Point NY United States Department of Bioengineering University of Washington Seattle WA United States Department of Psychology Carnegie Mellon University Pittsburgh PA United States

Memory is a complex process that spans multiple time-scales and stages, and, as expected, involves multiple brain regions. Traditionally, computational models of memory are either too abstract (Shiffrin & Steyvers, 1997) to be meaningfully connected to a biological substrate, or, when explicitly connected, are narrowly focused on one specific region and process (Blum & Abbott, 1996;Weber et al., 2017). By contrast, a comprehensive model of memory with a plausible neural interpretation would be extremely valuable to drive further research in memory function and dysfunction. In this paper, we attempt to fill in this gap by providing a detailed biological analysis of ACT-R’s declarative memory system. This system, developed over four decades, has evolved into a consistent framework that describes how memories are formed, retrieved, forgotten, mistaken, and merged. Building on existing mappings between some components and their biological counterpart, as well as the existing literature, this paper provides a comprehensive view of how the framework’s various computations map onto different brain regions, their network dynamics and functional connectivity, and biological structure. We also show that these mappings provide further insights and explanations for puzzling findings in the memory disorders literature. Finally, we outline the remaining gaps (such as the transition from episodic to semantic memory) and how they could be addressed by future research and modeling efforts. © 2023, The Author(s).

关键词： ACT-R Computational model Episodic memory Forgetting Hippocampus Long-term memory Prefrontal cortex Retrieval Semantic memory

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Learning Disentangled Representation for Multidimensional MR Image Reconstruction 46

Learning Disentangled Representation for Multidimensional MR...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Zhao, Ruiyang Wang, Zepeng Lam, Fan University of Illinois Urbana-Champaign Department of Electrical and Computer Engineering UrbanaIL United States University of Illinois Urbana-Champaign Department of Bioengineering UrbanaIL United States University of Illinois Urbana-Champaign Beckman Institute for Advanced Science and Technology UrbanaIL United States

ISBN: (纸本)9798350371499

We proposed a new way to represent and reconstruct multidimensional MR images. Specifically, a representation capable of disentangling different types of features in high-dimensional images was learned via training an autoencoder with separated sets of latent spaces for image style transfer, e.g., contrast or geometry transfer. A latent diffusion model was introduced to capture the distributions of the disentangled latents for constrained reconstruction. A new formulation was developed to integrate the pre-learned representation with other complementary constraints for reconstruction from sparse data. We demonstrated the ability of our model to disentangle contrast and geometry features in multicontrast MR images, and its effectiveness in accelerated T1 and T2 mapping. © 2024 IEEE.

关键词： Magnetic resonance imaging

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