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In recent days, deep learning technologies have achieved tremendous success in computer vision-related tasks with the help of large-scale annotated dataset. Obtaining such dataset for medical image analysis is very challenging. Working with the limited dataset and small amount of annotated samples makes it difficult to develop a robust automated disease diagnosis model. We propose a novel approach to generate synthetic medical images using generative adversarial networks (GANs). Our proposed model can create brain PET images for three different stages of Alzheimer's disease-normal control (NC), mild cognitive impairment (MCI), and Alzheimer's disease (AD).

Abstract-Body sensor networks (BSN) are emerging cyberphysical systems that promise to improve quality of life through improved healthcare, augmented sensing and actuation for the disabled, independent living for the elderly, and reduced healthcare costs. However, the physical nature of BSNs introduces new challenges. The human body is a highly dynamic physical environment that creates constantly changing demands on sensing, actuation, and quality of service. Movement between indoor and outdoor environments and physical movements constantly change the wireless channel characteristics. These dynamic application contexts can also have a dramatic impact on data and resource prioritization. Thus, BSNs must simultaneously deal with rapid changes to both top-down application requirements and bottom-up resource availability. This is made all the more challenging by the wearable nature of BSN devices, which necessitates a vanishingly small size and, therefore, extremely limited hardware resources and power budget. Current research is being performed to develop new principles and techniques for adaptive operation in highly dynamic physical environments, using miniaturized, energy-constrained devices. This paper describes a holistic cross-layer approach that addresses all aspects of the system, from low-level hardware design to higher-level communication and data fusion algorithms, to top-level applications.

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The effects of space radiation on the structural and electrical properties of MoS 2 field effect transistors (FETs) were investigated. The 1 MeV electronically equivalent International Space Station (ISS) track was used to apply fluence equivalent to the orbital for 10 (1.0×10 12 cm −2 ) and 30 years (3.0×10 12 cm −2 ) using the AP8 and AE8 models. X-ray photoelectron spectroscopy (XPS), Raman and photoluminescence (PL) spectra were recorded before and after irradiation. Electron irradiation produced strong desulfurization effects in MoS 2 FETs. The PL spectra before and after irradiation did not change significantly, while the E 2g 1 and A 1g Raman modes were red-and blue-shifted, respectively. The XPS results demonstrated a strong desulfurization effect of the electron beam on MoS 2 . This reduction indicates a much higher amount of irradiation-induced S vacancies compared to Mo vacancies. The electrical characteristics of the device were measured before and after irradiation. The increase in the channel leakage current after irradiation was attributed to the oxide trapping positive charges. MoS 2 FETs irradiated by the electron-beam demonstrated a decreased current. This phenomenon can be attributed to the combination of the states at the SiO 2 /MoS 2 interfaces and Coulomb scattering. Our study provides a deeper understanding of the influence of 1 MeV electron-beam irradiation on MoS 2 -based nano-electronic devices for future space applications.

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