To accommodate the wide range of input voltages supplied by redundant batteries and ensure an adequate hold-up time for communication systems during utility power failures, power supplies used in 5 G base stations typ...
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In recent years, many research results have discussed the mixed static-domino high-speed circuit. However, most research neglects to discuss how to implement their results. Silicon Intellectual Property plays very imp...
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This paper introduces CompressedMediQ, a novel hybrid quantum-classical machine learning pipeline specifically developed to address the computational challenges associated with high-dimensional multi-class neuroimagin...
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ISBN:
(数字)9798331519315
ISBN:
(纸本)9798331519322
This paper introduces CompressedMediQ, a novel hybrid quantum-classical machine learning pipeline specifically developed to address the computational challenges associated with high-dimensional multi-class neuroimaging data analysis. Standard neuroimaging datasets, such as large-scale MRI data from the Alzheimer’s disease Neuroimaging Initiative and Neuroimaging in Frontotemporal dementia, present significant hurdles due to their vast size and complexity. CompressedMediQ integrates classical HPC nodes for advanced MRI pre-processing and Convolutional Neural Network (CNN)-PCA-based feature extraction and reduction, addressing the limited-qubit availability for quantum data encoding in the NISQ era. This is followed by Quantum Support Vector Machine (QSVM) classification. By utilizing quantum kernel methods, the pipeline optimizes feature mapping and classification, enhancing data separability and outperforming traditional neuroimaging analysis techniques. Experimental results highlight the pipeline’s superior accuracy in dementia staging, validating the practical use of quantum machine learning in clinical diagnostics. despite the limitations of NISQ devices, this proof-of-concept demonstrates the transformative potential of quantum-enhanced learning, paving the way for scalable and precise diagnostic tools in healthcare and signal processing.
This paper introduces a high-efficiency two-stage power microinverter. In the dc-dc stage, a three-phase interleaved inverse buck-type converter is adopted to regulate the dc-bus voltage and improve the input ripples....
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Minimally Invasive Surgery (MIS) is a current prevalent technique for surgical operations. Compared with traditional surgical methods, MIS can reduce the post-surgical recovery time, as well as the costs and pain pati...
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This chapter integrated an active RFId tag with a thermal convection angular accelerometer on a flexible substrate. The first new idea was to make the device directly on a flexible substrate without any movable parts,...
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The real-time multi-task simulation of a 3-leg 6-dOF high performance platform system based on RT-Linux operation system is presented in this paper. The new architecture is setup by three extensible legs sliding on th...
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This research applied both the traditional and the fuzzy control methods for mobile satellite antenna tracking system design. Firstly, the antenna tracking and the stabilization loops were designed according to the tr...
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作者：
Yau, Yeu-TorngDepartment of Ph.D. Program
Prospective Technology of Electrical Engineering and Computer Science National Chin-Yi University of Technology Taichung No.57 Sec. 2 Zhongshan Rd. Taiping Dist Taichung41170 Taiwan
To provide a hold-up time function in dC-dC supplies for cell site stations or data centers, using a boost converter with a bulk output capacitor as a front-end converter stage is a simple and highly cost-effective so...
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This chapter proposes a MIMO fuel cell design by integrating both Ziegler-Nichols-based PId and intelligent fuzzy-neural controllers. Comparing with the other PId methods by Matlab simulation, the proposed system perf...
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