Medical image biomarkers of cancer promise improvements in patient care through advances in precision medicine. Compared to genomic biomarkers, image biomarkers provide the advantages of being non-invasive, and characterizing a heterogeneous tumor in its entirety, as opposed to limited tissue available via biopsy. We developed a unique radiogenomic dataset from a Non-Small Cell Lung Cancer (NSCLC) cohort of 211 subjects. The dataset comprises Computed Tomography (CT), Positron Emission Tomography (PET)/CT images, semantic annotations of the tumors as observed on the medical images using a controlled vocabulary, and segmentation maps of tumors in the CT scans. Imaging data are also paired with results of gene mutation analyses, gene expression microarrays and RNA sequencing data from samples of surgically excised tumor tissue, and clinical data, including survival outcomes. This dataset was created to facilitate the discovery of the underlying relationship between tumor molecular and medical image features, as well as the development and evaluation of prognostic medical image biomarkers.

obile wireless traffic has experienced explosive growth over the past decade, driven largely by the vast application of mobile devices. As application scenarios extended from traditional real-time voice communication to social networks, entertainment, and e-commerce, the number of devices and data rates keep growing exponentially. However, a broad consensus anticipates that the 4G mobile networks will not come close to meeting the demands that networks will face by 2020. Since the wireless link efficiency is approaching fundamental limits, future improvements in the capacity of wireless communication systems can be alternatively achieved by innovation and optimization in network schemes and infrastructures.The 4G/5G wireless networks have been characterized by heterogeneity due to mixed utilization of highly diversified access technologies. Therefore, network operators propose specific requirements to equipment providers for cost-efficient and energy-saving solutions. We introduce some new paradigms to address the above issues, including network function virtualization (NFV), software defined radio (SDR), and software defined network (SDN).First, NFV employs standard IT virtualization technology to consolidate multiple network equipment types onto industry standard high volume servers, switches, and storage devices. In this way, operators can architect networks toward deploying network services onto standard devices [1]. NFV enables delivering network functions without installing hardware equipment for every new service, making possible less investment in network equipment (CAPEX) and less expenditure on network management and operation (OPEX). It enbles the standard network appliance to migrate from one hardware platform to another.Second, SDR aims at implementing many modes by simply reconfiguring the radio with different software, as the name implies. The software may be pre-loaded in the device or downloaded through fixed data links or over-the-air (OTA). SDR has been successfully used in military communication systems and recently introduced to the consumer electronics market [2]. It plays a vital role in military applications with requirements of channel switching and modulation changing. Nowadays, the programmable SDR solution has become attractive as it supports rapid development of wireless standards and a short time to market.Third, SDN allows telecom software developers to control network resources in the same simple way as ordinary computing resources [3]. In order to support the programmability of the network by external applications, SDN addresses the separation of the control plane from the data plane with open interfaces between the centralized controller and packet forwarding devices. On one hand, the software-based controller functions as the control plane and is logically regarded as the core of the network intelligence; on the other hand, the network devices become simple packet forwarding devices representing the data plane.We study the possibility of integrating NFV with SDR/SDN for 4G/5G mo...

The Internet-of-Things (IoT) will significantly change both industrial manufacturing and our daily lives. Data collection and three-dimensional (3D) positioning of IoT devices are two indispensable services of such networks. However, in conventional networks, only terrestrial base stations (BSs) are used to provide these two services. On the one hand, this leads to high energy consumption for devices transmitting at cell edges. On the other hand, terrestrial BSs are relatively close in height, resulting in poor performance of device positioning in elevation. Due to their high maneuverability and flexible deployment, unmanned aerial vehicles (UAVs) could be a promising technology to overcome the above shortcomings. In this paper, we propose a novel UAV-assisted IoT network, in which a low-altitude UAV platform is employed as both a mobile data collector and an aerial anchor node to assist terrestrial BSs in data collection and device positioning. We aim to minimize the maximum energy consumption of all devices by jointly optimizing the UAV trajectory and devices' transmission schedule over time, while ensuring the reliability of data collection and required 3D positioning performance. This formulation is a mixed-integer non-convex optimization problem, and an efficient differential evolution (DE) based method is proposed for solving it. Numerical results demonstrate that the proposed network and optimization method achieve significant performance gains in both energy efficient data collection and 3D device positioning, as compared with a conventional terrestrial IoT network.

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