4 research outputs found
Transformer-Based Deep Learning Detector for Dual-Mode Index Modulation 3D-OFDM
In this paper, we propose a deep learning-based signal detector called
TransD3D-IM, which employs the Transformer framework for signal detection in
the Dual-mode index modulation-aided three-dimensional (3D) orthogonal
frequency division multiplexing (DM-IM-3D-OFDM) system. In this system, the
data bits are conveyed using dual-mode 3D constellation symbols and active
subcarrier indices. As a result, this method exhibits significantly higher
transmission reliability than current IM-based models with traditional maximum
likelihood (ML) detection. Nevertheless, the ML detector suffers from high
computational complexity, particularly when the parameters of the system are
large. Even the complexity of the Log-Likelihood Ratio algorithm, known as a
low-complexity detector for signal detection in the DM-IM-3D-OFDM system, is
also not impressive enough. To overcome this limitation, our proposal applies a
deep neural network at the receiver, utilizing the Transformer framework for
signal detection of DM-IM-3D-OFDM system in Rayleigh fading channel. Simulation
results demonstrate that our detector attains to approach performance compared
to the model-based receiver. Furthermore, TransD3D-IM exhibits more robustness
than the existing deep learning-based detector while considerably reducing
runtime complexity in comparison with the benchmarks
Deep Neural Network-Based Detector for Single-Carrier Index Modulation NOMA
In this paper, a deep neural network (DNN)-based detector for an uplink
single-carrier index modulation nonorthogonal multiple access (SC-IM-NOMA)
system is proposed, where SC-IM-NOMA allows users to use the same set of
subcarriers for transmitting their data modulated by the sub-carrier index
modulation technique. More particularly, users of SC-IMNOMA simultaneously
transmit their SC-IM data at different power levels which are then exploited by
their receivers to perform successive interference cancellation (SIC)
multi-user detection. The existing detectors designed for SC-IM-NOMA, such as
the joint maximum-likelihood (JML) detector and the maximum likelihood
SIC-based (ML-SIC) detector, suffer from high computational complexity. To
address this issue, we propose a DNN-based detector whose structure relies on
the model-based SIC for jointly detecting both M-ary symbols and index bits of
all users after trained with sufficient simulated data. The simulation results
demonstrate that the proposed DNN-based detector attains near-optimal error
performance and significantly reduced runtime complexity in comparison with the
existing hand-crafted detectors
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Ten golden rules for optimal antibiotic use in hospital settings: the WARNING call to action
Antibiotics are recognized widely for their benefits when used appropriately. However, they are often used inappropriately despite the importance of responsible use within good clinical practice. Effective antibiotic treatment is an essential component of universal healthcare, and it is a global responsibility to ensure appropriate use. Currently, pharmaceutical companies have little incentive to develop new antibiotics due to scientific, regulatory, and financial barriers, further emphasizing the importance of appropriate antibiotic use. To address this issue, the Global Alliance for Infections in Surgery established an international multidisciplinary task force of 295 experts from 115 countries with different backgrounds. The task force developed a position statement called WARNING (Worldwide Antimicrobial Resistance National/International Network Group) aimed at raising awareness of antimicrobial resistance and improving antibiotic prescribing practices worldwide. The statement outlined is 10 axioms, or “golden rules,” for the appropriate use of antibiotics that all healthcare workers should consistently adhere in clinical practice
Ten golden rules for optimal antibiotic use in hospital settings : the WARNING call to action
Abstract: Antibiotics are recognized widely for their benefits when used appropriately. However, they are often used inappropriately despite the importance of responsible use within good clinical practice. Effective antibiotic treatment is an essential component of universal healthcare, and it is a global responsibility to ensure appropriate use. Currently, pharmaceutical companies have little incentive to develop new antibiotics due to scientific, regulatory, and financial barriers, further emphasizing the importance of appropriate antibiotic use. To address this issue, the Global Alliance for Infections in Surgery established an international multidisciplinary task force of 295 experts from 115 countries with different backgrounds. The task force developed a position statement called WARNING (Worldwide Antimicrobial Resistance National/International Network Group) aimed at raising awareness of antimicrobial resistance and improving antibiotic prescribing practices worldwide. The statement outlined is 10 axioms, or "golden rules," for the appropriate use of antibiotics that all healthcare workers should consistently adhere in clinical practice