In this paper, we consider a 2 × 2 space-time line coded (STLC) system having two-transmit and two-receive antennas. To improve the secrecy rate of the STLC system, in which an illegitimate receiver eavesdrops the information delivered from the STLC transmitter to the STLC receiver, we propose an artificial noise (AN) injection method. By exploiting the STLC structure, a novel AN for the STLC is designed and its optimal power loading factor is derived. Numerical results verify that the proposed secure STLC systems with the designed AN injection and the power control method can significantly improve the secrecy rate compared to the conventional STLC systems. It is observed that the proposed method is more effective if there is a significant gap between the main-channel and the eavesdropper-channel gains.
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http://dx.doi.org/10.3390/e21050515 | DOI Listing |
Comput Methods Programs Biomed
January 2025
Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, 400714, China.
Background And Objective: Cloud-based Deep Learning as a Service (DLaaS) has transformed biomedicine by enabling healthcare systems to harness the power of deep learning for biomedical data analysis. However, privacy concerns emerge when sensitive user data must be transmitted to untrusted cloud servers. Existing privacy-preserving solutions are hindered by significant latency issues, stemming from the computational complexity of inner product operations in convolutional layers and the high communication costs of evaluating nonlinear activation functions.
View Article and Find Full Text PDFPLoS One
January 2025
Trinity Centre for Biomedical Engineering, Trinity College Dublin, Dublin, Ireland.
Electroencephalographic signals are obtained by amplifying and recording the brain's spontaneous biological potential using electrodes positioned on the scalp. While proven to help find changes in brain activity with a high temporal resolution, such signals are contaminated by non-stationary and frequent artefacts. A plethora of noise reduction techniques have been developed, achieving remarkable performance.
View Article and Find Full Text PDFJ Dent Sci
January 2025
Institute of Statistics, National Yang Ming Chiao Tung University, Hsinchu City, Taiwan.
Background/purpose: In this study, we utilized magnetic resonance imaging data of the temporomandibular joint, collected from the Division of Oral and Maxillofacial Surgery at Taipei Veterans General Hospital. Our research focuses on the classification and severity analysis of temporomandibular joint disease using convolutional neural networks.
Materials And Methods: In gray-scale image series, the most critical features often lie within the articular disc cartilage, situated at the junction of the temporal bone and the condyles.
J Imaging Inform Med
January 2025
Leiden University Medical Center (LUMC), Leiden, the Netherlands.
Rising computed tomography (CT) workloads require more efficient image interpretation methods. Digitally reconstructed radiographs (DRRs), generated from CT data, may enhance workflow efficiency by enabling faster radiological assessments. Various techniques exist for generating DRRs.
View Article and Find Full Text PDFISA Trans
January 2025
Institute of Artificial Intelligence and Future Networks, Beijing Normal University at Zhuhai, Zhuhai, China; BNU-HKBU United International College Tangjiawan, Rd. JinTong 2000#, Zhuhai, China. Electronic address:
In this paper, a novel recursive hierarchical parametric identification method based on initial value optimization is proposed for Wiener-Hammerstein systems subject to stochastic measurement noise. By transforming the traditional Wiener-Hammerstein system model into a generalized form, the system model parameters are uniquely expressed for estimation. To avoid cross-coupling between estimating block-oriented model parameters, a hierarchical identification algorithm is presented by dividing the parameter vector into two subvectors containing the coupled and uncoupled terms for estimation, respectively.
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