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Optimal control of electric automated buses in intercity lines: scenario-based analysis of the Pareto-optimal solutions

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IFAC-PapersOnLine 2024年第10期58卷 37-42页

作者： C. Pasquale S. Sacone S. Siri A. Ferrara Department of Informatics Bioengineering Robotics and Systems Engineering University of Genova Italy Department of Electrical Computer and Biomedical Engineering University of Pavia Italy

This paper deals with the optimal control of electric and automated buses that have to follow an intercity line, where some stops are equipped with a flash-charging infrastructure to charge the batteries, while others are not. In order to control these buses, it is necessary to account for the traffic conditions along the roads of the bus line, and to minimize two objectives related to the deviations from the bus timetable and the lack of energy, at the end of the bus trip, with respect to a desired final energy level. The resulting optimal control problem is a multi-objective linear quadratic problem with both continuous and integer variables. In this paper, we propose and apply two methods based on lexicographic ordering aimed at determining the Pareto-optimal solutions of the control problem in twelve scenarios inspired by a real case.

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Modified CNN VoxNet Based Depthwise Separable Convolution for Voxel-Driven Body Orientation Classification

Modified CNN VoxNet Based Depthwise Separable Convolution fo...

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2024 IEEE International Conference on Imaging Systems and Techniques, IST 2024

作者： Riansyah, Moch. Iskandar Putra, Oddy Virgantara Priyadi, Ardyono Sardjono, Tri Arief Yuniarno, Eko Mulyanto Purnomo, Mauridhi Hery Institut Teknologi Sepuluh Nopember Department of Electrical Engineering Indonesia Telkom University Department of Electrical Engineering Indonesia Universitas Darussalam Gontor Department of Informatics Ponorogo Indonesia Institut Teknologi Sepuluh Nopember Department of Electrical Engineering Surabaya Indonesia Institut Teknologi Sepuluh Nopember Department of Biomedical Engineering Surabaya Indonesia Institut Teknologi Sepuluh Nopember Department of Electrical Engineering Department of Computer Engineering Surabaya Indonesia

ISBN: (纸本)9798350378214

In this research, LiDAR sensor technology introduces a new representation of point cloud data for tasks in 3D object recognition. Point clouds provide rich information that can be utilized to predict the orientation pose of the human body. The orientation of the human body is crucial for various needs such as autonomous vehicles, robotics, and human activity monitoring. However, the inherently unstructured nature and permutation invariance of point cloud data pose significant challenges in accurately predicting human body orientation using 3D data. In this paper, we propose a solution by introducing a volumetric method based on CNN to effectively recognize human body ori-entation. A novel approach is proposed by embedding depthwise sparse convolution into 2D CNN to process voxel data of human body orientation. We evaluate the performance using KITTI data divided into four orientation classes: east, west, north, and south. Additionally, we compare it with other architectures using the ModelNet10 and ModelNet40 datasets. The comparison with other architectures shows superior performance with accuracies of 84.31 % for the KITTI dataset, 86.67 % for ModelNet10, and 75.20 % for ModelNet40. This evidence strongly supports the effectiveness of our proposed method, highlighting its potential in enhancing 3D object recognition systems significantly. © 2024 IEEE.

关键词： CNN depthwise sparse convolution human body orientation lidar point cloud

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Respiration and heartbeat rates estimation using IR-UWB non-contact radar sensor recordings: A pre-clinical study

Respiration and heartbeat rates estimation using IR-UWB non-...

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2023 Workshops of the EDBT/ICDT Joint Conference, EDBT/ICDT-WS 2023

作者： Pentari, Anastasia Manousos, Dimitrios Kassiotis, Thomas Rigas, George Tsiknakis, Manolis CreteGR-700 13 Greece PD Neurotechnology Ltd. Ioannina Greece Biomedical Informatics and eHealth Department of Electrical and Computer Engineering Hellenic Mediterranean University Greece

Sleep study is of major importance for the assessment of sleep apneas, sleep stages or the diagnosis of sleep disorders. Although there exists a variety of tools for the evaluation of sleep, including the gold standard polysomnography (PSG) or high-tech wearable devices, the last decade there is also an increasing interest in the use of ultra wide-band (UWB) radar sensors for non-contact medical studies. The objective of this study is to develop a pre-clinical environment for the measurement of two important vital signs of the human functions, i.e., the respiratory and heartbeat rates, through UWB radar recordings of chest motion. For this purpose, at first we composed a simulating chest and heart motion architecture which represents these two basic body functions in parallel. After that, with the employment of a UWB-radar, we performed extensive recordings of simulator's displacements, and we concluded to the mathematical estimation of the respiratory and heartbeat rates, compared to the initial frequencies given to the simulated procedure. Our experimental results prove that we can estimate both rates in an accurate manner and even if these pre-clinical tests are made under ideal conditions, the UWB-radar can further be used for clinical assessment, with promising perspectives. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). CEUR Workshop Proceedings (http://***)

关键词： Diagnosis

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Supervised learning applied to electrocardiogram statistical features for the detection of premature ventricular contraction

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Research on biomedical engineering 2025年第1期41卷 1-12页

作者： Issa, Khouloud Rammal, Abbas Assaf, Rabih Ghandour, Ahmad Electrical and Communication Engineering Department College of Engineering Phoenicia University Zahrani South Lebanon Statistics and Informatics Department Faculty of Science Lebanese University Beirut Lebanon Mathematics and Computer Sciences Department Faculty of Arts and Sciences Lebanese American University Beirut Lebanon Department of Mathematics Faculty of Arts and Sciences Holy Spirit University of Kaslik Jounieh Lebanon Biomedical Engineering Department Faculty of Engineering Islamic University of Lebanon Wardenieh30014 Lebanon

Purpose: The development of an automated premature ventricular contraction (PVC) detection system has significant implications for early intervention and treatment decisions. This study aims to develop a novel approach using various supervised machine learning (ML) methods for detecting PVCs in electrocardiogram (ECG) recordings. Methodology: To achieve this, we extracted ten distinct statistical and temporal features from 33 long-term ECG recordings from the benchmark MIT-BIH arrhythmia database (MIT-BIH-AD), capturing significant characteristics from the signals. We then investigated the effectiveness of traditional ML algorithms in identifying PVCs based on these features, namely support vector machine (SVM), decision tree (DT), naïve Bayes (NB), k-nearest neighbor (KNN), linear discriminant analysis (LDA), and artificial neural network (ANN). Results: Among these classifiers, the SVM, KNN, ANN, and DT classifiers demonstrated exceptional discriminatory power and classification performance, yielding near-perfect area under the receiver operating characteristic curve (AUC-ROC) values of 97.3%, 95.4%, 95%, and 94.1%, respectively. On the other hand, the SVM classifier emerged as the most accurate, achieving an overall accuracy of 97.39%, indicative of making correct predictions. Conclusion: These findings underscore the generalization of the proposed approach, especially since we worked on an extended dataset encompassing different types of heartbeats to discriminate PVCs from other heartbeat types, not only normal ones, and providing closer alignment with real-world scenarios. Subsequently, the robustness and accuracy of these models highlight their suitability for clinical applications and their potential for efficient implementation and deployment in the diagnosis of cardiovascular diseases (CVDs), as they demand minimal computational resources and time. © The Author(s), under exclusive licence to The Brazilian Society of biomedical engineering 2025.

关键词： Decision trees

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Quantifying Strain Dependence of Multi-Frequency Shearwave Elasticity Imaging

Quantifying Strain Dependence of Multi-Frequency Shearwave E...

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2023 IEEE Western New York Image and Signal Processing Workshop, WNYISPW 2023

作者： Khan, Siladitya Goswami, Soumya Feng, Fan Doyley, Marvin M. McAleavey, Stephen A. University of Rochester Departments of Biomedical and Computer Engineering RochesterNY14627 United States University of Rochester Rochester Center for Biomedical Ultrasound RochesterNY14627 United States University of Rochester Goergen Institute for Data Science RochesterNY14627 United States University of Rochester Departments of Electrical and Computer Engineering RochesterNY14627 United States

ISBN: (纸本)9798350329698

Nonlinear shear modulus (NLSM) can potentially differentiate benign and malignant pathologies. Previous studies demonstrated the discriminatory value of mapping quantitative estimates of material non-linearity, yet estimates remain biased due to unaccounted shear-wave dispersion within the tissue. We demonstrate a finite element simulation of an inversion technique that combines traditional quasi-static and dynamic elasticity imaging to generate a multi-frequency estimate of the third-order shear modulus A(x,z,ω) over a broad bandwidth (200-600 Hz). We show that our reconstruction results are superior to those obtained by traditional group speed by a factor of two in terms of contrast enhancement measures. Experimental ultrasound elastography on a custom oil-in-gelatin emulsion confirms the presence of dispersion in the NLSM parameter. Constitutive hyper-viscoelastic model responses of spectral NLSM are simulated based on matched linear shear modulus data of experimental phantoms. Hyper-elastic Ogden with a Voigt loss is shown to describe NLSM behavior at frequencies greater than 400 Hz. Reconstruction accuracy in the presence of simulated tracking and correlation noise demonstrates the robustness of our estimates. © 2023 IEEE.

关键词： Elastic moduli

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Sparsity-Aware Hardware-Software Co-Design of Spiking Neural Networks: An Overview

arXiv

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arXiv 2024年

作者： Aliyev, Ilkin Svoboda, Kama Adegbija, Tosiron Fellous, Jean-Marc Department of Electrical & Computer Engineering Departments of Psychology and Biomedical Engineering University of Arizona TucsonAZ United States

Spiking Neural Networks (SNNs) are inspired by the sparse and event-driven nature of biological neural processing, and offer the potential for ultra-low-power artificial intelligence. However, realizing their efficiency benefits requires specialized hardware and a co-design approach that effectively leverages sparsity. We explore the hardware-software co-design of sparse SNNs, examining how sparsity representation, hardware architectures, and training techniques influence hardware efficiency. We analyze the impact of static and dynamic sparsity, discuss the implications of different neuron models and encoding schemes, and investigate the need for adaptability in hardware designs. Our work aims to illuminate the path towards embedded neuromorphic systems that fully exploit the computational advantages of sparse SNNs. © 2024, CC BY.

关键词： Neural network models

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Enhanced Driver Stress Prediction from Multiple Biosignals via CNN Encoder-Decoder Model 46

Enhanced Driver Stress Prediction from Multiple Biosignals v...

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

作者： Singh Roha, Vishal Gaber Gendy, Maggie Ezzat Pavan, Kaveti Deserno, Thomas M. Ganapathy, Nagarajan Yuce, Mehmet R. Monash University Dept. of Electrical & Computer Systems Melbourne Australia Indian Institute of Technology Hyderabad Dept. of Biomedical Engineering Hyderabad India Technical University of Braunschweig Plri Lab for Medical Informatics Brunswick Germany

ISBN: (纸本)9798350371499

In this work, we present PhysioFuseNet, a novel framework designed to enhance driver stress state classification. PhysioFuseNet integrates a CNN-based encoder-decoder model with multimodal biosignal fusion. Using a driving simulator, different multimodal signals were acquired, namely electrocardiography, electrodermal activity, photoplethysmography, and respiration rate from (N = 25) healthy subjects. The experiment is of 35 minutes duration and contains different stress states (baseline (5 minutes), while normal, cognitive, and emotional sessions for 10 minutes). Multimodal features are extracted and employed in an encoder-decoder network. Extracted encoder features are combined through intermediate fusion and fed to support vector machine (SVM) and random forest (RF) classifiers. Experimental results demonstrate the efficacy of our approach, outperforming previous methods by achieving accuracies of 0.95 and 0.94 for SVM and RF, respectively. Notably, the framework excels in classifying emotional and cognitive stress states. In summary, the proposed framework could be useful in stress assessment in real-time and clinical conditions. © 2024 IEEE.

关键词： Photoplethysmography

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StackAMP: Stacking-Based Ensemble Classifier for Antimicrobial Peptide Identification

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第11期5卷 5666-5675页

作者： Karim, Tasmin Shaon, Md. Shazzad Hossain Ali, Md. Mamun Ahmed, Kawsar Bui, Francis M. Chen, Li Department of Computer Science and Engineering Birulia Dhaka Savar1216 Bangladesh University of Saskatchewan Division of Biomedical Engineering SaskatoonSKS7N 5A9 Canada University of Saskatchewan Department of Electrical and Computer Engineering SaskatoonSKS7N 5A9 Canada Health Informatics Research Laboratory Department of Computer Science and Engineering Birulia Dhaka Savar1216 Bangladesh Mawlana Bhashani Science and Technology University Group of Bio-photomati Information and Communication Technology Tangail Santosh1902 Bangladesh

Antimicrobial peptides (AMPs) play a vital role in the immune defence systems of various organisms and have garnered significant attention for their potential applications in biotechnology and medicine. There are several approaches to identifying AMPs including clinical isolation and characterization, functional genomics, microbiology techniques, and others. However, these methods are mostly expensive, time-consuming, and require well-equipped labs. To overcome these challenges, machine learning models are a potential solution due to their robustness and high predictive capability with less time and cost. In this study, we explored the efficacy of stacking-based ensemble machine-learning techniques to identify AMPs with higher accuracy and precision. Five distinct feature extraction methods, namely amino acid composition, dipeptide composition, moran autocorrelation, geary autocorrelation, and pseudoamino acid composition, were employed to represent the sequence characteristics of peptides. To build robust predictive models, different traditional machine learning algorithms were applied. Additionally, we developed a novel stacking classifier, aptly named StackAMP, to harness the collective power of these algorithms. Our results demonstrated the exceptional performance of the proposed StackAMP ensemble method in AMP identification, achieving an accuracy of 99.97%, 99.93% specificity, and 100% sensitivity. This high accuracy underscores the effectiveness of our approach, which has promising outcomes for the rapid and accurate identification of AMPs in various biological contexts. This study not only contributes to the growing body of knowledge in the field of AMP recognition but also offers a practical tool with potential applications in drug discovery, biotechnology, and disease prevention. © 2024 IEEE.

关键词： Amino acids

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Classifying Upper Extremity Motor Function Using Deep Learning and Third-Person Video Data

Classifying Upper Extremity Motor Function Using Deep Learni...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Tran, Tan Chang, Lin-Ching Lum, Peter The Catholic University of America Department of Electrical Engineering and Computer Science WashingtonDC United States New Jersey Institute of Technology Department of Informatics NewarkNJ United States The Catholic University of America Department of Biomedical Engineering WashingtonDC United States

ISBN: (纸本)9798350362480

Post-stroke rehabilitation of upper extremity (UE) motor function is essential. Despite the widespread use of UE rehabilitation in clinical settings, assessing the success of these treatments is challenging. Methods for evaluating UE motor function include task performance under clinical supervision, patient self-reports, and data analysis from wearable devices equipped with accelerometers and gyroscopes. In prior research, we demonstrated that machine learning and deep learning models using data from a single wrist-worn accelerometer sensor could accurately differentiate between functional and non-functional UE movements in stroke patients. To overcome the limitations of single wrist-worn accelerometer sensors - challenges in capturing the full context of functional movements - this study presents a new deep learning (DL) based framework designed to classify functional and non-functional arm movements in videos captured from a conventional camera. The system is entirely automated and comprises two DL networks. The first network performs human pose estimation, extracting 2D pose key points of the paretic arm(s) and torso on 2-second sequences of the frames. The second network then uses these 2D pose landmarks to classify the movement sequence as functional or non-functional. This system offers two key benefits for rehabilitation. First, it can automatically generate initial annotations for video frames, significantly reducing the time needed for manual labeling. Second, analyzing the effectiveness of UE rehabilitation through third-person videos allows for objective outcome measurement of UE treatments in stroke survivors' home environments. © 2024 IEEE.

关键词： Accelerometers

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Simulated deep CT characterization of liver metastases with high-resolution filtered back projection reconstruction

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Visual Computing for Industry,Biomedicine,and Art 2024年第1期7卷 257-271页

作者： Christopher Wiedeman Peter Lorraine Ge Wang Richard Do Amber Simpson Jacob Peoples Bruno De Man Department of Electrical and Computer Engineering Rensselaer Polytechnic InstituteTroyNY 12180USA GE Research-Healthcare NiskayunaNY 12309USA Department of Biomedical Engineering Rensselaer Polytechnic InstituteTroyNY 12180USA Department of Radiology Memorial Sloan Kettering Cancer CenterNew YorkNY 10065USA Biomedical Computing and Informatics Queen’s UniversityKingstonON K7L 3N6Canada

Early diagnosis and accurate prognosis of colorectal cancer is critical for determining optimal treatment plans and maximizing patient outcomes,especially as the disease progresses into liver *** tomography(CT)is a frontline tool for this task;however,the preservation of predictive radiomic features is highly dependent on the scanning protocol and reconstruction *** hypothesized that image reconstruction with a highfrequency kernel could result in a better characterization of liver metastases features via deep neural *** kernel produces images that appear noisier but preserve more sinogram information.A simulation pipeline was developed to study the effects of imaging parameters on the ability to characterize the features of liver *** pipeline utilizes a fractal approach to generate a diverse population of shapes representing virtual metastases,and then it superimposes them on a realistic CT liver region to perform a virtual CT scan using *** of 10,000 liver metastases were generated,scanned,and reconstructed using either standard or high-frequency *** data were used to train and validate deep neural networks to recover crafted metastases characteristics,such as internal heterogeneity,edge sharpness,and edge fractal *** the absence of noise,models scored,on average,12.2%(α=0.012)and 7.5%(α=0.049)lower squared error for characterizing edge sharpness and fractal dimension,respectively,when using high-frequency reconstructions compared to ***,the differences in performance were statistically insignificant when a typical level of CT noise was simulated in the clinical *** results suggest that high-frequency reconstruction kernels can better preserve information for downstream artificial intelligence-based radiomic characterization,provided that noise is *** work should investigate the informationpreserving kernels in datasets with clinical labels.

关键词： Radiomics Deep learning Computed tomography Colorectal liver metastases Virtual clinical trials Image reconstruction

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