Thermally assisted spin transfer torque [TAS + STT] is a new switching approach for magnetic tunnel junction [MTJ] nanopillars that represents the best trade-off between data reliability, power efficiency and density. In this paper, we present a compact model for MTJ switched by this approach, which integrates a number of physical models such as temperature evaluation and STT dynamic switching models. Many experimental parameters are included directly to improve the simulation accuracy. It is programmed in the Verilog-A language and compatible with the standard IC CAD tools, providing an easy parameter configuration interface and allowing high-speed co-simulation of hybrid MTJ/CMOS circuits.
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http://dx.doi.org/10.1186/1556-276X-6-368 | DOI Listing |
Artif Organs
January 2025
BioCirc Research Laboratory, School of Biomedical Engineering, Science, and Health Systems, Drexel University, Philadelphia, Pennsylvania, USA.
Background: Safe and effective pediatric blood pumps continue to lag far behind those developed for adults. To address this growing unmet clinical need, we are developing a hybrid, continuous-flow, magnetically levitated, pediatric total artificial heart (TAH). Our hybrid TAH design, the Dragon Heart (DH), integrates both an axial flow and centrifugal flow blood pump within a single, compact housing.
View Article and Find Full Text PDFJ Clin Med
January 2025
Hospital Virgen de la Arrixaca, 30120 Murcia, Spain.
Accurate segmentation of the left ventricular myocardium in cardiac MRI is essential for developing reliable deep learning models to diagnose left ventricular non-compaction cardiomyopathy (LVNC). This work focuses on improving the segmentation database used to train these models, enhancing the quality of myocardial segmentation for more precise model training. We present a semi-automatic framework that refines segmentations through three fundamental approaches: (1) combining neural network outputs with expert-driven corrections, (2) implementing a blob-selection method to correct segmentation errors and neural network hallucinations, and (3) employing a cross-validation process using the baseline U-Net model.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Department of Computer Science, Faculty of Sciences and Humanities Sciences, Majmaah University, Al Majmaah 11952, Saudi Arabia.
Impedance-based biosensing has emerged as a critical technology for high-sensitivity biomolecular detection, yet traditional approaches often rely on bulky, costly impedance analyzers, limiting their portability and usability in point-of-care applications. Addressing these limitations, this paper proposes an advanced biosensing system integrating a Silicon Nanowire Field-Effect Transistor (SiNW-FET) biosensor with a high-gain amplification circuit and a 1D Convolutional Neural Network (CNN) implemented on FPGA hardware. This attempt combines SiNW-FET biosensing technology with FPGA-implemented deep learning noise reduction, creating a compact system capable of real-time viral detection with minimal computational latency.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Acropolis Restoration Service, Hellenic Ministry of Culture, 10555 Athens, Greece.
This study focuses on the geotechnical evaluation of the foundation conditions of the Agrippa Monument at the Acropolis of Athens, aiming to propose interventions to improve stability and reduce associated risks. The assessment reveals highly uneven foundation conditions beneath the monument. A thorough collection of bibliographic references and geotechnical surveys was conducted, classifying geomaterials into engineering-geological units and evaluating critical parameters for geotechnical design.
View Article and Find Full Text PDFCancers (Basel)
December 2024
65+ Outpatient Clinic, Amalia Fleming General Hospital, 14, 25th Martiou Str., 15127 Melissia, Greece.
: Melanoma, an aggressive form of skin cancer, accounts for a significant proportion of skin-cancer-related deaths worldwide. Early and accurate differentiation between melanoma and benign melanocytic nevi is critical for improving survival rates but remains challenging because of diagnostic variability. Convolutional neural networks (CNNs) have shown promise in automating melanoma detection with accuracy comparable to expert dermatologists.
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