In geometric computer vision, 3D registration is a critical task for various applications. 3D medical image registration, as one important application of 3D registration, holds substantial clinical potentials for supp...
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Energy efficiency can be improved significantly by monitoring and optimizing utilization patterns, which is essential due to the substantial impact of human behavior on domestic energy consumption. Thus, this study de...
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The COVID-19 pandemic has emphasized the critical need for effective health safety measures in public spaces. This paper introduces a sensor-based automatic multifunctional sanitization system designed to automate han...
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ISBN:
(数字)9798331518523
ISBN:
(纸本)9798331518530
The COVID-19 pandemic has emphasized the critical need for effective health safety measures in public spaces. This paper introduces a sensor-based automatic multifunctional sanitization system designed to automate hand sanitization, body temperature detection, and whole-body disinfection. Utilizing an Arduino microcontroller and various sensors, the system operates seamlessly in high-traffic areas like hospitals, offices, and malls. Key processes include touch-free hand sanitization, temperature screening (<100°F), and sodium hypochlorite fogging for comprehensive disinfection, achieving an average process time of 10–15 seconds per user. The system incorporates audio and visual indicators for completion, ensuring continuous operation without human intervention. Experimental evaluations demonstrate high accuracy in thermal screening (±1°C) and effective disinfection coverage, with minor limitations in sensor performance under extreme environmental conditions. These results highlight the system's efficiency and potential for scalable implementation in public health safety.
The analysis of ECG, EEG and EMG signals requires biomedical signal processing which needs low-power high-performance VLSI architectures to achieve real-time processing. This paper introduces an optimized VLSI archite...
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ISBN:
(数字)9798331521394
ISBN:
(纸本)9798331521400
The analysis of ECG, EEG and EMG signals requires biomedical signal processing which needs low-power high-performance VLSI architectures to achieve real-time processing. This paper introduces an optimized VLSI architecture which enables biomedical signal processing through energy-efficient digital filtering along with feature extraction as well as pipeline-based clock gating. The proposed architecture combines a distributed arithmetic FIR filter without multipliers to reduce complexity and it integrates a low-power feature extraction system that detects ECG R-peaks and EEG spectral energy and EMG muscle activity effectively. The system uses dynamic clock gating together with power gating to cut down both dynamic power usage and leakage power. The experimental findings show that the developed VLSI architecture both reduces power usage through low hardware complexity while keeping high accuracy levels for biomedical signal processing. The system delivers its performance measurement through power dissipation and area efficiency along with processing speed which enables its use in wearable and implantable medical devices.
The kidney is an important organ in the body to excrete metabolic waste, and the glomerulus is an essential structure for the kidney to play a role in blood filtration. Abnormal glomerular numbers are associated with ...
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Image fusion is a technique used in image processing to create a more comprehensive representation by combining features and data from several images. Incorporating medical images from several imaging modalities, such...
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ISBN:
(数字)9798331518523
ISBN:
(纸本)9798331518530
Image fusion is a technique used in image processing to create a more comprehensive representation by combining features and data from several images. Incorporating medical images from several imaging modalities, such as computed tomography (CT) scans, Positron Emission Tomography (PET) and magnetic resonance imaging (MRI), into a single set of data is what multi-modal medical image fusion is all about. Better visualization of anatomical structures and clinical conditions is the outcome of this integration, which increases diagnostic accuracy by using the strengths of each modality. In this paper, MRI, CT, and PET scans are used as experimental modalities. This review aims to compare various multi modal medical image approach based on Multi-resolution Singular Value Decomposition (MSVD), Principal Component Analysis (PCA), Discrete Wavelet Transform (DWT), and Wavelet Packet Decomposition (WPD). This paper focuses on exploring latest conventional and non-conventional research being done using these domains. It also compares these methods based on various image quality parameters, and various quantitative checks. Based on this comparison, PCA shows the best results in the comparison as the overall visual and parametric quality of fusion results are better than compared methods.
The component of a system charged with ensuring the security of its users is among its most crucial parts. It has been demonstrated that using a password or login that is too basic makes you vulnerable to hackers and ...
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For the treatment of diseases like lymphedema and chronic venous insufficiency, compression therapy is crucial, but conventional methods frequently produce less-than-ideal results because they are impersonal. This pap...
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ISBN:
(数字)9798331521394
ISBN:
(纸本)9798331521400
For the treatment of diseases like lymphedema and chronic venous insufficiency, compression therapy is crucial, but conventional methods frequently produce less-than-ideal results because they are impersonal. This paper presents a novel approach that uses cloud computing and decision trees to create personalized compression therapy plans in healthcare settings. The proposed system uses cloud infrastructure's scalability and computational power to handle massive amounts of patient data collected in real-time from wearable Internet of Things (IoT) devices. Through device integration, pertinent physiological parameters can be continuously monitored, allowing prompt therapy plan modifications in response to changing patient requirements. Decision Trees, a machine learning (ML) algorithm selected for its capacity to comprehend complicated datasets and produce useful insights, are essential to the system's operation. Decision trees dynamically customize compression therapy regimens using patient data analysis to maximize patient comfort and treatment effectiveness. Early system trials have produced encouraging outcomes, including notable increases in patient satisfaction and treatment outcomes. This strategy prioritizes the delivery of personalized healthcare, which increases therapeutic efficacy and fosters a patient-centered treatment experience. The combination of cloud computing and Decision Trees is a progressive step forward in the healthcare industry, providing scalable solutions that can be tailored to the specific needs of each patient and method for future developments in personalized medical care.
The paper presents a comprehensive study on the application of Machine Learning (ML) in enhancing the contrast of biomedical images. This research is pivotal in addressing the challenges of low visibility and detail i...
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ISBN:
(数字)9798350385328
ISBN:
(纸本)9798350385335
The paper presents a comprehensive study on the application of Machine Learning (ML) in enhancing the contrast of biomedical images. This research is pivotal in addressing the challenges of low visibility and detail in medical imaging, which are crucial for accurate diagnosis and treatment. The paper introduces innovative ML-based Histogram Equalization (ML - HE), leveraging deep learning algorithms and advanced image processing methods, to significantly improve the quality of biomedical images. These techniques enhance image clarity, detail, and overall contrast without compromising the integrity of the original data. This paper seeks to explore the integration of ML, specifically Reservoir computing, with traditional image enhancement methods, creating a synergistic approach that leverages the strengths of both ML and conventional techniques and expedites image enhancements in near real-time. This hybrid approach is shown to be more effective in handling diverse and complex imaging scenarios encountered in biomedical applications. The study also discusses the implications of these advancements for medical professionals, highlighting how ML-enhanced images can lead to more accurate diagnoses, better patient outcomes, and advancements in medical research. Overall, this paper sheds light on the transformative potential of ML in revolutionizing biomedical imaging, setting a new standard for image quality and diagnostic precision in healthcare.
Images are present in everything these days, from satellite photography to medical scans, and they have a profound impact on how people view and understand the world. The daily generation of an ever-increasing volume ...
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ISBN:
(数字)9798331522100
ISBN:
(纸本)9798331522117
Images are present in everything these days, from satellite photography to medical scans, and they have a profound impact on how people view and understand the world. The daily generation of an ever-increasing volume of image data has made it imperative to identify effective ways to convey these images. This research presents a novel picture recovery approach that makes use of Chaotic Maps for increased security and Compressive Sensing for effective compression. Compressive Sensing makes use of the sparsity seen in images to minimize the number of data points required. Chaotic Map is used in encryption, is also to guarantee the security of the compressed data. The Neural network model built to recover images is a hybrid of Conditional Generative Adversarial Networks (C-GANs), patch discriminator and U-net generator. This allows for high-quality image reconstruction. This approach is suitable for usage in healthcare and marine life.
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