The increasing need for efficient monitoring of parameters in challenging industrial environments, such as basic sanitation, has led to the search for integrated and user-friendly solutions that can address specific c...
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ISBN:
(数字)9798331507466
ISBN:
(纸本)9798331507473
The increasing need for efficient monitoring of parameters in challenging industrial environments, such as basic sanitation, has led to the search for integrated and user-friendly solutions that can address specific challenges of this setting. This project aims to develop a solution to meet monitoring requirements in water treatment plants. The proposed solution involves the development of both hardware and software for collecting data from the water treatment process using sensors and transmitting this information to a server structure, database, and communication interface with the cloud-based user. The effectiveness of the proposed device is being evaluated in six water treatment units located in the states of Alagoas, Mato Grosso, Paraná, Rio de Janeiro, and São Paulo. Preliminary results indicate that the proposed solution is capable of monitoring the variables involved in the water flocculation stage and generating a comprehensive set of information about the process over time.
This paper introduces HAAQI-Net, a non-intrusive music audio quality assessment model for hearing aid users. Unlike traditional methods such as Hearing Aid Audio Quality Index (HAAQI), which requires intrusive referen...
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This paper introduces HAAQI-Net, a non-intrusive music audio quality assessment model for hearing aid users. Unlike traditional methods such as Hearing Aid Audio Quality Index (HAAQI), which requires intrusive reference signal comparisons, HAAQI-Net offers a more accessible and computationally efficient alternative. Leveraging a bidirectional long short-term memory architecture with attention mechanisms and features extracted from a pre-trained BEATs model, it can predict HAAQI scores directly from music audio clips and hearing loss patterns. The experimental results demonstrate that, compared to the traditional HAAQI as the reference, HAAQI-Net achieves a linear correlation coefficient (LCC) of 0.9368, a Spearman's rank correlation coefficient (SRCC) of 0.9486, and a mean squared error (MSE) of 0.0064, while significantly reducing the inference time from 62.52 seconds to 2.54 seconds. Furthermore, a knowledge distillation strategy was applied, reducing the parameters by 75.85% and inference time by 96.46%, while maintaining strong performance (LCC: 0.9071, SRCC: 0.9307, MSE: 0.0091). To expand its capabilities, HAAQI-Net was adapted to predict subjective human scores, mean opinion score (MOS), by fine-tuning. This adaptation significantly improved the prediction accuracy. Furthermore, the robustness of HAAQI-Net was evaluated under varying sound pressure level (SPL) conditions, revealing optimal performance at a reference SPL of 65 dB, with the accuracy gradually decreasing as SPL deviated from this point. The advancements in subjective score prediction, SPL robustness, and computational efficiency position HAAQI-Net as a reliable solution for music audio quality assessment, significantly contributing to the development of efficient and accurate models in audio signal processing and hearing aid technology.
Sudden cardiac arrest (SCA) poses a significant health challenge, necessitating accurate predictions of neurological outcomes in comatose patients, where good outcomes are defined as the recovery of most cognitive fun...
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In this paper we report the synthesis and characterization of tungsten oxide doped silver oxide nanostructures. The effect of silver doping on the optical and sensing properties of tungsten doped silver oxide is studi...
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Surface features of natural materials such as cuticles, shells, and leaves continue to be used as models for creating sophisticated infrastructure, landscape, and microdevice designs. Optical microscopy is the easiest...
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We present the Quantum Kernel-Based Long Short-Term Memory (QK-LSTM) network, which integrates quantum kernel methods into classical LSTM architectures to enhance predictive accuracy and computational efficiency in cl...
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ISBN:
(数字)9798331531591
ISBN:
(纸本)9798331531607
We present the Quantum Kernel-Based Long Short-Term Memory (QK-LSTM) network, which integrates quantum kernel methods into classical LSTM architectures to enhance predictive accuracy and computational efficiency in climate time-series forecasting tasks, such as Air Quality Index (AQI) prediction. By embedding classical inputs into high-dimensional quantum feature spaces, QK-LSTM captures intricate nonlinear dependencies and temporal dynamics with fewer trainable parameters. Leveraging quantum kernel methods allows for efficient computation of inner products in quantum spaces, addressing the computational challenges faced by classical models and variational quantum circuit-based models. Designed for the Noisy Intermediate-Scale Quantum (NISQ) era, QK-LSTM supports scalable hybrid quantum-classical implementations. Experimental results demonstrate that QK-LSTM outperforms classical LSTM networks in AQI forecasting, showcasing its potential for environmental monitoring and resource-constrained scenarios, while highlighting the broader applicability of quantum-enhanced machine learning frameworks in tackling large-scale, high-dimensional climate datasets.
The purpose of this letter is to study the design and explore vertically stacked complementary tunneling field-effect transistors (CTFETs) using CFET technology for emerging technology nodes. As a prior work, the CTFE...
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Microsatellite instability (MSI) is a pivotal genetic marker influencing the efficacy of immunotherapy in colorectal cancer. Traditional MSI examination often requires additional genetic or immunohistochemical tests, ...
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ISBN:
(数字)9798331518622
ISBN:
(纸本)9798331518639
Microsatellite instability (MSI) is a pivotal genetic marker influencing the efficacy of immunotherapy in colorectal cancer. Traditional MSI examination often requires additional genetic or immunohistochemical tests, whereas histology images, widely available in colorectal cancer diagnosis, offer a valuable alternative for MSI prediction. Although Transformer-based models have demonstrated promising outcomes in predicting MSI from histology images, they are hampered by traditional local attention mechanisms that struggle to capture long-range interdependencies and establish a comprehensive global receptive field. In this study, we introduce DiNAT-MSI, a novel framework for histology-based MSI prediction that incorporates the Dilated Neighborhood Attention Transformer (DiNAT). This model enhances global context recognition and substantially expands receptive fields, all without additional computational burden. Our results demonstrate that DiNAT-MSI achieves a superior patientwise AUROC compared to ResNet18 and Swin Transformer, along with commendable explainability. Our work not only illustrates a more accessible diagnostic tool for leveraging histological data but also underscores the potential of Transformerbased models with sophisticated attention designs in advancing precision medicine for colorectal cancer patients.
Artificial lipid droplets (aLDs) provide a useful tool to explore the multiple functionalities of intracellular lipid droplets (LDs). In this study we explored the dynamics and potential multidisciplinary applications...
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This paper presents a fast Affine Motion Estimation (AME) of Versatile Video Coding (VVC) Standard, based on Machine Learning and using Random Forest (RF) classification method. This encoding approach develops an RF m...
This paper presents a fast Affine Motion Estimation (AME) of Versatile Video Coding (VVC) Standard, based on Machine Learning and using Random Forest (RF) classification method. This encoding approach develops an RF model for each block size. The models were trained with information extracted during the VVC encoding process of the current, parent, and neighboring Coding Units (CU). Each model is applied to predict whether the Affine Motion Estimation (AME) will be skipped or not for that CU size. The proposed solution achieves a reduction of 20% on average in AME encoding time, with an insignificant impact of 0.07% on BD-BR.
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