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RenalCare: Personalized Alerting System for Renal Cancer Recurrence Prevention via IoT and ML

RenalCare: Personalized Alerting System for Renal Cancer Rec...

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International conference on Communications and signal processing

作者： P Gajalakshmi J Raja Department of CSE Adhiparasakthi Engineering College Affilated to Anna University Melmaruvathur India Department of ECE Adhiparasakthi Engineering College Affilated to Anna University Melmaruvathur India

ISBN: (数字)9798350353068

ISBN: (纸本)9798350353075

The early detection of renal cancer recurrence plays a critical role in improving patient outcomes and quality of life. In this study, we propose a novel approach for an Early Warning System for Renal Cancer Recurrence leveraging wearable IoT devices, smartphone integration, cloud-based data processing, and advanced machine learning and deep learning algorithms. Specifically, we employ a range of wearable sensors, including pulse oximeters, blood pressure monitors, temperature sensors, and activity trackers, to capture relevant physiological parameters associated with renal cancer recurrence. Optional implantable sensors, such as glucose monitors and biomarker sensors, are also considered for enhanced monitoring capabilities. Smartphone integration serves as a central hub for data aggregation, enabling real-time monitoring, alerts, and feedback mechanisms for both patients and healthcare providers through mobile applications. Sensor data is securely transmitted to cloud-based platforms for centralized storage and processing, where data fusion techniques are implemented to integrate sensor data with electronic health records (EHR), imaging data, and other clinical information for comprehensive analysis. Machine learning and deep learning algorithms are developed and trained on integrated datasets to detect patterns indicative of renal cancer recurrence, analyzing multi-modal data sources to identify subtle indicators of recurrence. Finally, a real-time alerting system is implemented to trigger notifications to healthcare providers when abnormal patterns or deviations from baseline are detected, enabling timely intervention and proactive management strategies. This comprehensive Early Warning System enables continuous monitoring, early detection of abnormalities, and personalized interventions, ultimately leading to improved patient outcomes and quality of care in the management of renal cancer recurrence.

关键词： Temperature sensors Deep learning Temperature measurement signal processing algorithms Sensor fusion Real-time systems Internet of Things

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Energy Efficient Image Transmission Over Semantic Communication Enabled UAV Swarm Networks

Energy Efficient Image Transmission Over Semantic Communicat...

引用

International conference on Wireless Communications and signal processing (WCSP)

作者： Yixiao Feng Yining Wang Bo Zhang Huadong Dai Academy of Military Science Beijing China Advanced Institute of Big Data Beijing China

ISBN: (数字)9798350390643

ISBN: (纸本)9798350390650

In this paper, a semantic communication framework is proposed for unmanned aerial vehicle (UAV) swarm formation flight. In the studied model, a leader extracts semantic information from the aerial image and transmits it to each follower using limited transmission power. The borders of obstacles are referred to semantic information of the aerial image. Based on the received semantic information, each follower reconstructs semantic maps for trajectory planning so as to maintain formation and avoid obstacles. We use the metric of Intersection over Union (IoU) to evaluate the effectiveness of the proposed semantic communication network. The problem is formulated as an optimization problem whose goal is to maximize the sum of IoUs of all followers. To solve this problem, a semantic information selection and power allocation (SISPA) algorithm based on reinforcement learning (RL) framework is proposed. The proposed SISPA algorithm enables the leader to evaluate the importance of semantic information, and, thus efficiently allocating transmission power and selecting important semantic information to improve the sum of IoUs. Simulation results demonstrate that, compared with the traditional wireless communication networks without semantic communication techniques, the proposed approach yields a 17.4% improvement in terms of the sum of IoUs.

关键词： Measurement Trajectory planning Simulation Wireless networks signal processing algorithms signal processing Semantic communication Autonomous aerial vehicles Resource management Optimization

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Imbalanced Data Classification using Optimized Support Vector Machine for IoMT Application 2

Imbalanced Data Classification using Optimized Support Vecto...

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2nd International conference on Ambient Intelligence in Health Care, ICAIHC 2023

作者： Pattanayak, Binod Kumar Mohanty, Mihir Narayan Satpathy, Smitarani Arjun Das, Jay Bijay Dept. of CSE ITER Odisha Bhubaneswar751030 India Advanced Signal Processing Lab ITER Odisha Bhubaneswar751030 India

ISBN: (纸本)9798350344882

In this digital age, most of the applications need to be Internet of Things based for easy access. Biomedical research has made significant growth related to it. The visual pathological information provides comparatively better information as compared to signals that help physicians with further analysis and suggestions. Medical data often exhibit class imbalance, where the number of samples in different classes is not proportional. This imbalance can pose significant challenges to traditional classification algorithms. This report explores the application of Support Vector Machines (SVM) to imbalanced medical data classification, discussing the inherent issues, techniques for addressing class imbalance, and experimental results showcasing the effectiveness of SVM in handling imbalanced medical datasets. As a result, the retrieval process over the internet will be faster. In this paper, authors have considered three different cancerous image data of breast cancer, brain tumor, and liver cancer for analysis and detection using a machine learning approach. The data is collected from the UCI machine learning repository. The statistical features like class, radius, and area are considered and optimized using the Brain Storm Optimization algorithm for good accuracy. The optimized features are applied to the support vector machine classifier. Further, use for different kernels such as radial basis function (RBF), linear, polynomial, and sigmoid are used with SVM and verified for comparison. It is found that the RBF kernel provides SVM better accuracy as compared to other kernels. © 2023 IEEE.

关键词： Brain

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From Linear to Nonlinear Unfolded Condat-Vú Algorithm for Spectro-Polarimetric Hight-Constrast Image Recovery

From Linear to Nonlinear Unfolded Condat-Vú Algorithm for S...

引用

European signal processing conference (EUSIPCO)

作者： E. Chappon N. Pustelnik J. Tachella L. Denneulin A. Ferrari M. Langlois Laboratoire de Physique ENSL CNRS UMR 5672 Lyon France Laboratoire de Recherche de l'EPITA EPITA Le Kremlin-Bicêtre France Universite Côte d'Azur Observatoire de la Côte d'Azur CNRS Lab. J.-L. Lagrange France Univ Lyon Univ. Lyon 1 ENS de Lyon CNRS Centre de Recherche Astrophysique de Lyon UMR5574 Saint-Genis-Laval France

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

Studying circumstellar environments is crucial for understanding exoplanets and stellar systems. Instruments like SPHERE can extract information about these environments by leveraging advanced image reconstruction methods, possibly based on deep learning. This work focuses on unfolded proximal neural networks based on Condat- Vii iterations and proposes a new nonlinear formulation. To evaluate and compare the performance of the proposed reconstruction strategies, two datasets dedicated to circumstellar environments analysis in the context of high-contrast imagery have been created offering different level of complexity in the evaluation of the performance.

关键词： Deep learning Training Image color analysis Neural networks signal processing algorithms signal processing Image reconstruction Standards Synthetic data Finite difference methods

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Investigating Delay Compensation within the Context of Frequency-Aided PLL

Investigating Delay Compensation within the Context of Frequ...

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International conference on Localization and GNSS (ICL-GNSS)

作者： Xinhua Tang Xinmin Zhu Shuguo Pan Tao Zhang Zhe Yan Laura Ruotsalainen Key Laboratory of Micro-inertial Instrument and Advanced Navigation Technology of Ministry of Education Southeast University Nanjing China Department of Computer Science University of Helsinki Helsinki Finland

ISBN: (数字)9798350380781

ISBN: (纸本)9798350380798

The transition from analog GNSS receivers to digital ones has spurred the emergence of various GNSS baseband signal processing algorithms. Among these, Frequency-aided PLL has been widely investigated in different forms, including FLL-assisted PLL, VFLL, etc. Great contributions have been made in the practical implementation and evaluation of different forms of Frequency-aided PLLs, but the impact of frequency aid information delay has not been thoroughly analyzed quantitatively. To date, only preliminary impact analysis in S domain is available in few literature sources. This paper mainly addresses the problem in Z domain that is more aligned with practical implementations. Two distinct types of Frequency-aided PLL are firstly proposed and a quasi-FLL-assisted PLL considering the frequency aid delay is chosen to be analyzed. Additionally, delay compensation is also derived using the Controlled-Root method to counteract the impact of delay, providing a valuable insight into the problem of frequency aid delay in other models.

关键词： Location awareness Global navigation satellite system Baseband signal processing algorithms Receivers Stability analysis Delays

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Review of Recent Development in EMG signal Classification Methods

Review of Recent Development in EMG Signal Classification Me...

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Engineering, Science and advanced Technology (ICESAT), International conference on

作者： Sara W. Almola Eanass U. Taha Department of Medical Instrumentation Techniques Engineering Technical Engineering College Northern Technical University Mosul Iraq

Characterizing electromyographic (EMG) signals is essential in diagnosing neuromuscular diseases. Different classifiers have been developed to build accurate and highly efficient EMG signal characterization algorithms. This paper reviews some classification approaches used in EMG characterization and discusses the most recent achievements in this field, emphasizing neuromuscular disease. The methods studied are classified according to their methodology, and a synopsis of the significant findings related to each way is provided. Surface EMG is recently more relative to signal processing and artificial intelligence for moving organs. On the other hand, traditional EMG is under the scope of surgical cases by following different algorithms. The specified algorithm is our interest to start work from the endpoint of a light spot like Wavelet Transform (WT), Discrete Fourier transform (DFT), Higher order statistics (HOS), Artificial Neural Network (ANN), Fuzzy Logic (FL), Empirical Mode Decomposition (EMD), Independent Component Analysis (ICA), Principle Component Analysis (PCA) and Autoregressive (AR).

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Mental Disorder Detection using Non-Orthogonal Adaptive Constrained IVABMGGMM

Mental Disorder Detection using Non-Orthogonal Adaptive Cons...

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International conference on advanced Technologies for signal and Image processing (ATSIP)

作者： Ali Algumaei Muhammad Azam Nizar Bouguila Concordia Institute for Information Systems Engineering Concordia University Quebec Montreal Canada School of Computer Science and Technology Algoma University Sault Ste. Marie Ontario Canada

ISBN: (数字)9798350351484

ISBN: (纸本)9798350351491

The constrained independent component analysis (cICA) algorithm has proven remarkable effectiveness in integrating prior information into the framework of independent component analysis (ICA). However, the necessity of orthogonality for decoupling the constraints on individual sources restricts the optimization space and consequently the separation performance of cICA algorithms. To overcome this limitation, we propose a decoupling method called non-orthogonal adaptive constrained independent vector analysis with a bounded multivariate generalized Gaussian mixture model (non-orthogonal acIVABMGGMM), designed to address the drawbacks of independent component analysis in modeling multivariate data. This structure has the capability to incorporate prior knowledge regarding the sources or the mixing coefficients into the cost function of IVA. A robust decoupling strategy is needed to enable this integration. We also identify the brain regions impacted by Alzheimer’s disease by using the extracted components from our non-orthogonal acIVABMGGMM model. We demonstrate the superior performance of non-orthogonal acIVABMGGMM over the basic IVA model through experiments conducted on both simulated and real medical imaging data.

关键词： Adaptation models Analytical models Mental disorders Image processing Independent component analysis Brain modeling Vectors

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When Sparse Graph Representation Learning Falls into Domain Shift: Feature Augmentation for Cross-Domain Graph Meta-Learning

When Sparse Graph Representation Learning Falls into Domain ...

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International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Simin Niu Xun Liang Sensen Zhang Zhiyu Li Xuan Zhang Wu Bo Hanyu Wang Shichao Song Mengwei Wang Jiawei Yang School of Information Renmin University of China Beijing China Institute for Advanced Algorithms Research Shanghai China Guanghua School of Management Peking University Beijing China Xiangjiang Laboratory Central South University Hunan China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Graph Meta-learning methods have improved the performance of few-shot node classification by means of applying meta-learning to the data in non-Euclidean domains. However, most works focus on adopting a single domain, ignoring the fact that tasks in various domains may be distinct, which can cause overfitting problems and thus limit generalizability. To tackle this challenge, we propose a novel Graph Meta-learning framework called Feature-Enhanced Cross-domain Graph Meta-learning that consists of two crucial modules: 1) A feature information extraction module that aims to capture discriminative node importance and simulate various node feature distributions under distinct domains; 2) A heterogeneous graph encoder module that leverages the enhanced node features and topological information to generate task-specific node embeddings with simple fine-tuning. Moreover, we meta-learn the parameters involved to ensure the generalizability in the unseen domains. Results show that our method markedly outperforms the existing state-of-the-art methods.

关键词： Metalearning Representation learning signal processing Feature extraction Information retrieval Acoustics Data mining Speech processing Overfitting

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Human Motion Tracking: A Comparative Analysis of Recent Advancements

Human Motion Tracking: A Comparative Analysis of Recent Adva...

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International conference on advanced Robotics and Intelligent Systems (ARIS)

作者： Yan-Ru Lee Jin-Siang Shaw Fu-Shin Lee Department of Computer Science & Information Engineering National Taipei University of Technology Taipei Taiwan Department of Mechanical Engineering National Taipei University of Technology Taipei Taiwan Department of Smart Living Huafan University New Taipei City Taiwan

ISBN: (数字)9798350362572

ISBN: (纸本)9798350362589

This paper explores the advancements in human motion-tracking technology within the past three years, analyzing research that presents novel methods for capturing and analyzing human movement. We examine the methodologies employed by each approach, including sensor-based systems, radar technology, Wi-Fi signal utilization, lightweight networks for real-time action recognition, and a system for joint camera localization and human motion tracking. This paper compares each surveyed research's advantages, disadvantages, and limitations, highlighting their practical implications and potential applications. Finally, this review discusses the future directions and challenges within human motion tracking.

关键词： Simultaneous localization and mapping Tracking signal processing algorithms Radar Radar tracking Cameras Real-time systems

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Comparative Analysis of Wavelet Transform on fPCG signals for Denoising and Peak Extraction

Comparative Analysis of Wavelet Transform on fPCG Signals fo...

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Intelligent signal processing and Effective Communication Technologies (INSPECT), IEEE International conference on

作者： Megh Mhatre Geetha Narayanan Anjali Deshpande Biomedical Engineering Vidyalankar Instiute of Technology Mumbai India Systems and Control Indian Institute of Technology Bombay Mumbai India

ISBN: (数字)9798350379525

ISBN: (纸本)9798350379532

Fetal Phonocardiogram (fPCG) is a bio-signal obtained from recording the heart sounds from stethoscopes or auditory transducers for auscultations of FHR. Analysis of this data helps to give early diagnosis of any irregularity throughout Fetal development process. However, the analysis of this signal poses many challenges due to noise components like Motion Artifacts and Respiration of the Foetus and Mother, Uterine Contractions, Power Line Interference, Sensor Noise as well as Background Noise. Many approaches have been presented by the researchers to extract characteristic heart sound peaks from these signals to estimate the heart rate and to detect the abnormalities like arrhythmia. In this work, we present the results of application of advanced signal processing techniques such as wavelet transform with adaptive thresholding, to extract the peaks of the fPCG signal. Simulations have been performed on the set of real data from fetus as well as simulated signals available in the resources. Simulation results show that Discrete Wavelet Transform with adaptive thresholding is a better technique for denoising the signal and isolating the heartbeat peaks as compared to other studies discussed in various resources. This study also includes evaluation of various parameters employed in the processes including the verification of peaks by setting up a window of specific time to analyze the peak count within each window for consistent peak extraction results. Various combinations of wavelets with different thresholding techniques have been studied and evaluated for accuracy.

关键词： Wavelet transforms Adaptation models Transducers Finite impulse response filters Simulation Noise reduction Noise signal processing algorithms Stethoscope Wavelet analysis

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