We combine generative adversarial network (GAN) with light microscopy to achieve deep learning super-resolution under a large field of view (FOV). By appropriately adopting prior microscopy data in an adversarial training, the neural network can recover a high-resolution, accurate image of new specimen from its single low-resolution measurement. Its capacity has been broadly demonstrated via imaging various types of samples, such as USAF resolution target, human pathological slides, fluorescence-labelled fibroblast cells, and deep tissues in transgenic mouse brain, by both wide-field and light-sheet microscopes. The gigapixel, multi-color reconstruction of these samples verifies a successful GAN-based single image super-resolution procedure. We also propose an image degrading model to generate low resolution images for training, making our approach free from the complex image registration during training dataset preparation. After a welltrained network being created, this deep learning-based imaging approach is capable of recovering a large FOV (~95 mm 2 ), high-resolution (~1.7 μm) image at high speed (within 1 second), while not necessarily introducing any changes to the setup of existing microscopes.

Abstract-The capacity and error probability of orthogonal space-time block codes (STBCs) are considered for pulse-amplitude modulation/phase shift keying/quadrature-amplitude modulation (PAM/PSK/QAM) in fading channels. The approach is based on an equivalent scalar additive white Gaussian noise channel with a channel gain proportional to the Frobenius norm of the matrix channel for the STBC. Using this effective channel, capacity and probability of error expressions are derived for PSK/PAM/QAM modulation with space-time block coding. Rayleigh-, Ricean-, and Nakagami-fading channels are considered. As an application, these results are extended to obtain the capacity and probability of error for a multiuser direct sequence code-division multiple-access system employing space-time block coding.Index Terms-Code division multiple access (CDMA), fading channels, information rates, phase shift keying, pulse amplitude modulation, quadrature amplitude modulation, Rayleigh channels, Ricean channels.

Deep learning (DL) has emerged as an effective tool for channel estimation in wireless communication systems, especially under some imperfect environments. However, even with such unprecedented success, DL methods still serve as black boxes and the lack of explanations on their internal mechanism severely limits further improvement and extension. In this paper, we present a preliminary theoretical analysis on DL based channel estimation for multiple-antenna systems to understand and interpret its internal mechanism. Deep neural network (DNN) with rectified linear unit (ReLU) activation function is mathematically equivalent to a set of local linear functions corresponding to different input regions.Hence, the DL estimator built on it can achieve universal approximation to a large family of functions by making efficient use of piecewise linearity. We demonstrate that DL based channel estimation does not restrict to any specific signal model and will approach to the minimum mean-squared error (MMSE) estimation in various scenarios without requiring any prior knowledge of channel statistics. Therefore, DL based channel estimation outperforms or is comparable with traditional channel estimation. Simulation results confirm the accuracy of the proposed interpretation and demonstrate the effectiveness of DL based channel estimation under both linear and nonlinear signal models.

This paper focuses on resilient control of networked control systems (NCSs) under the denial of service (DoS) attacks which is characterized by a Markov process. Firstly, the packets dropout are modeled as Markov process according to the game between attack strategies and defense strategies. Then, an NCS under such game results is modeled as a Markovian jump linear system and four theorems are proved for the system stability analysis and controller design.Finally, a numerical example is used to illustrative the application of these theorems.

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BackgroundComplicated skin and skin structure infections (cSSSIs) frequently result in hospitalization with significant morbidity and mortality.MethodsIn this phase 3b/4 parallel, randomized, open-label, comparative study, 531 subjects with cSSSI received tigecycline (100 mg initial dose, then 50 mg intravenously every 12 hrs) or ampicillin-sulbactam 1.5-3 g IV every 6 hrs or amoxicillin-clavulanate 1.2 g IV every 6-8 hrs. Vancomycin could be added at the discretion of the investigator to the comparator arm if methicillin-resistant Staphylococcus aureus (MRSA) was confirmed or suspected within 72 hrs of enrollment. The primary endpoint was clinical response in the clinically evaluable (CE) population at the test-of-cure (TOC) visit. Microbiologic response and safety were also assessed. The modified intent-to-treat (mITT) population comprised 531 subjects (tigecycline, n = 268; comparator, n = 263) and 405 were clinically evaluable (tigecycline, n = 209; comparator, n = 196).ResultsIn the CE population, 162/209 (77.5%) tigecycline-treated subjects and 152/196 (77.6%) comparator-treated subjects were clinically cured (difference 0.0; 95% confidence interval [CI]: -8.7, 8.6). The eradication rates at the subject level for the microbiologically evaluable (ME) population were 79.2% in the tigecycline treatment group and 76.8% in the comparator treatment group (difference 2.4; 95% CI: -9.6, 14.4) at the TOC assessment. Nausea, vomiting, and diarrhea rates were higher in the tigecycline group.ConclusionsTigecycline was generally safe and effective in the treatment of cSSSIs.Trial registrationClinicalTrials.gov NCT00368537

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