Wireless connectivity delay, disruption, or failure can significantly affect the performance of wireless-enabled medical devices, which in turn causes potential risks to the patient. Notably, the challenges related to connectivity provisioning are exacerbated in the fifth-generation (5G)-enabled healthcare use cases where mobility is utilized. In this article, we describe relevant 5G-enabled healthcare use cases involving mobility and identify the connectivity challenges that they face. We then illustrate practical implementation considerations, tradeoffs, and future research directions for enabling reliable 5G healthcare transmissions. This is done through simulation of connected ambulances as an example use-case.

Future advances in deep learning and its impact on the development of artificial intelligence (AI) in all fields depend heavily on data size and computational power. Sacrificing massive computing resources in exchange for better precision rates of the network model is recognized by many researchers. This leads to huge computing consumption, while satisfactory results are not always expected when computing resources are limited. Therefore, it is necessary to find a balance between resources and model performance to achieve satisfactory results. This article proposes a self-adaptive quality of experience (QoE) framework, DeepNetQoE, to guide the training of deep networks. A self-adaptive QoE model is set up that relates the model's accuracy with the computing resources required for training which will allow the experience value of the model to improve. To maximize the experience value, a resource allocation model and solutions need to be established. Finally, we carry out experiments based on four network models to analyze the experience values with respect to the crowd counting example. Experimental results show that the proposed DeepNetQoE is capable of adaptively obtaining a high experience value according to user requirements and therefore guiding users to determine the computational resources allocated to the network models.

We developed a magnetic resonance compatible real-time, three-dimensional imaging ultrasound probe for motion management of radiation therapy for liver cancer. The probe contains an 18,000-element, 46.8 mm × 21.5 mm matrix array constructed from three tiled transducer modules with integrated beamforming ASICs. The center frequency and -6 dB fractional bandwidth of the probe was 3.6 MHz and 85 percent respectively. Ferromagnetic materials in the acoustic stack, flex interconnect and electronics boards were greatly minimized for magnetic resonance compatibility. The probe and cable were shielded to minimize the impact of radiofrequency noise on both the ultrasound and magnetic resonance images. The probe's low-profile, side-viewing design allows it to be strapped to a patient so that images may be acquired hands-free. We present simultaneously acquired ultrasound and 3 Tesla magnetic resonance images with minimal artifacts in both images.

Ultrasound current source density imaging (UCSDI) is a noninvasive technique for mapping electric current fields in 4D (space + time) with the resolution of ultrasound imaging. This approach can potentially overcome limitations of conventional electrical mapping procedures often used during treatment of cardiac arrhythmia or epilepsy. However, at physiologic currents, the detected acoustoelectric (AE) interaction signal in tissue is very weak. In this work, we evaluated coded ultrasound excitation (chirps) for improving the sensitivity of UCSDI for mapping the electrocardiogram (ECG) in a live rabbit heart preparation. Results confirmed that chirps improved detection of the AE signal by as much as 6.1 dB compared to a square pulse. We further demonstrated mapping the ECG using a clinical intracardiac catheter, 1 MHz ultrasound transducer and coded excitation. B-mode pulse echo and UCSDI revealed regions of high current flow in the heart wall during the peak of the ECG. These improvements to UCSDI are important steps towards translation of this new technology to the clinic for rapidly mapping the cardiac activation wave.

The windows into brain function given us by the instruments of neuroimaging each are murky and their view is limited. Simultaneous collection of data from multiple modalities offers the potential to overcome the weaknesses of any tool alone. We argue that the combination of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) offers observations - and hypothesis testing - not possible using either single instrument. Because of their safety profiles and their non-invasive natures, EEG fMRI are among the best available devices for the study of human brain. These methods are complementary. EEG is fast, operating in a time domain comparable to single unit activity, but its localizing power is poor and the field of view is limited. While fMRI has the highest spatial resolution of any noninvasive imaging method and can reveal multiple centers of brain activity implicated in cognitive tasks, it is very slow compared to mental activity and is a poor choice for studying rapidly evolving processes. Here, we address theoretical models of the coupling between EEG and fMRI signals based on cellular physiology and energetics and argue that both tools observe principally synaptic activity. We discuss the technical problems of mutual interference then present several models of brain rhythms for which the joint EEG and fMRI observations provide significant evidence.

A high data rate terrestrial and satellite network was implemented to transfer medical images and data. This article describes the a optimization of the workstations and switching equipment incorporated into the network. Topics discussed in this article include tuning of the network software, the configuration of the Sun Microsystems workstations, the FORE Systems asynchronous transfer mode switches, as well as the throughput results of two telemedicine experiments undertaken by Mayo's physician staff. The technical staff was successful in achieving the data throughput needed by the telemedicine software; particularly important was the proper determination of peak throughput and TCP window sizes to ensure optimum use of the resources available on the Sun Microsystems and Hewlett Packard workstations.

Big data create values for business and research, but pose significant challenges in terms of networking, storage, management, analytics and ethics. Multidisciplinary collaborations from engineers, computer scientists, statisticians and social scientists are needed to tackle, discover and understand big data. This survey presents an overview of big data initiatives, technologies and research in industries and academia, and discusses challenges and potential solutions.

Device-to-Device (D2D) communications has been realized as an effective means to improve network throughput, reduce transmission latency, and extend cellular coverage in 5G systems. Network coding is a well-established technique known for its capability to reduce the number of retransmissions. In this article, we review state-of-the-art network coding in relay-based D2D communications, in terms of application scenarios and network coding techniques. We then apply two representative network coding techniques to dual-hop D2D communications and present an efficient relay node selecting mechanism as a case study. We also outline potential future research directions, according to the current research challenges. Our intention is to provide researchers and practitioners with a comprehensive overview of the current research status in this area and hope that this article may motivate more researchers to participate in developing network coding techniques for different relay-based D2D communications scenarios.

With the massive integration of distributed renewable energy sources (RESs) into the power system, the demand for timely and reliable network quality monitoring, control, and fault analysis is rapidly growing. Following the successful deployment of Phasor Measurement Units (PMUs) in transmission systems for power monitoring, a new opportunity to utilize PMU measurement data for power quality assessment in distribution grid systems is emerging. The main problem however, is that a distribution grid system does not normally have the support of an infrastructure network. Therefore, the main objective in this paper is to develop a Machine-to-Machine (M2M) communication network that can support wide ranging sensory data, including high rate synchrophasor data for real-time communication. In particular, we evaluate the suitability of the emerging IEEE 802.11ah standard by exploiting its important features, such as classifying the power grid sensory data into different categories according to their traffic characteristics. For performance evaluation we use our hardware in the loop grid communication network testbed to access the performance of the network.

In the US, cardiovascular disease, cancer, and diabetes are in the top ten leading causes of death categories. The diseases compromise US life-expectancy and account for significant US health-care costs. This observational study investigates the US population's 1997-2017 Centers for Disease Control and Prevention (CDC) WONDER ICD-10 mortality records to extract the prevalence rates for leading causes of death by diabetes, neoplasms (cancers), and diseases of the circulatory system. The variables of race and age are examined for each disease in order to evaluate demographic and age-group risks. To document the public health burden from these three chronic conditions, mortality data from CDC WONDER was analyzed using MS-Excel and Statistical Analysis System (SAS) software. The general trend of deaths by diabetes, neoplasms, and diseases of the circulatory system has been progressively decreasing nationally; however, a significantly higher trend in mortality rates is observed for the Black or African American populations. Furthermore, over the 1997-2017 observational period, the crude mortality rates for the 45-54 (middle-age) and lower age-groups are below national mortality rate averages but are troublingly increasing for diabetes and notably, for the diseases of the circulatory system, the (younger) 25-34 age-group had a crude mortality rate increase of 6.78%.

Named Data Networking (NDN) architectural features, including multicast data delivery, stateful forwarding, and in-network data caching, have shown promise for applications such as video streaming and file sharing. However, collaborative applications, requiring a multi-producer participation introduce new NDN design challenges. In this paper, we highlight these challenges in the context of the Network Time Protocol (NTP) and one of its most widely-used deployments for NTP server discovery, the NTP pool project. We discuss the design requirements for the support of NTP and NTP pool and present general directions for the design of a time synchronization protocol over NDN, coined Named Data Networking Time Protocol ().

Delay-sensitive applications have been driving the move away from cloud computing, which cannot meet their low-latency requirements. Edge computing and programmable switches have been among the first steps toward pushing computation closer to end-users in order to reduce cost, latency, and overall resource utilization. This article presents the "compute-less" paradigm, which builds on top of the well known edge computing paradigm through a set of communication and computation optimization mechanisms (e.g.,, in-network computing, task clustering and aggregation, computation reuse). The main objective of the compute-less paradigm is to reduce the migration of computation and the usage of network and computing resources, while maintaining high Quality of Experience for end-users. We discuss the new perspectives, challenges, limitations, and opportunities of this compute-less paradigm.

5G and Beyond 5G (B5G) communication networks, with their characteristics of increasing speed, connectivity, reliability, availability and capacity while reducing latency, have the potential to transform the healthcare sector by opening possibilities for novel healthcare use cases and applications. Service level agreements (SLAs) can help enable these new healthcare use cases by documenting the communication requirements, performance standards, and roles and responsibilities of the stakeholders involved in providing safe and effective 5G-enabled healthcare to patients. However, the peculiarities and nuances of 5G implementations give rise to gaps in this area that should be addressed to streamline the implementation of 5G technology in healthcare. This magazine article highlights the key challenges and describes open research questions related to SLAs for 5G-healthcare systems. Addressing the research challenges in this space will help in developing robust SLAs that can ensure that device manufacturers, network service providers, users, and regulatory authorities share a common framework to safely integrate 5G & B5G technology in healthcare.