Satellites at work (Space in the seventies)

The use of satellites in the areas of communications, meteorology, geodesy, navigation, air traffic control, and earth resources technology is discussed. NASA contributions to various programs are reviewed

Spatial and seasonal relationships between Pacific harbor seals (Phoca vitulina richardii) and their prey, at multiple scales

Knowing where pinnipeds forage is vital to managing and protecting their populations, and for assessing potential interactions with fisheries. We assessed the spatial relationship between the seasonal distribution of Pacific harbor seals (Phoca vitulina richardii) outfitted with satellite transmitters and the seasonal distributions of potential harbor seal prey species in San Francisco Bay, California. Pearson’s correlation coefficients were calculated between the number of harbor seal locations in an area of the San Francisco Bay and the abundance of specific prey species in the same area. The influence of scale on the analyses was assessed by varying the scale of analysis from 1 to 10 km. There was consistency in the prey species targeted by harbor seals year-round, although there were seasonal differences between the most important prey species. The highest correlations between harbor seals and their prey were found for seasonally abundant benthic species, located within about 10 km of the primary haul-out site. Probable foraging habitat for harbor seals was identified, based on areas with high abundances of prey species that were strongly correlated with harbor seal distribution. With comparable local data inputs, this approach has potential application to pinniped management in other areas, and to decisions about the location of marine reserves designed to protect these species

A Regional Assessment of Florida Manatees (Trichechus manatus latirostris) and the Caloosahatchee River, Florida

(58pp.

Adaptive Neuro-Fuzzy Inference System for Dynamic Load Balancing in 3GPP LTE

ANFIS is applicable in modeling of key parameters when investigating the performance and functionality of wireless networks. The need to save both capital and operational expenditure in the management of wireless networks cannot be over-emphasized. Automation of network operations is a veritable means of achieving the necessary reduction in CAPEX and OPEX. To this end, next generations networks such WiMAX and 3GPP LTE and LTE-Advanced provide support for self-optimization, self-configuration and self-healing to minimize human-to-system interaction and hence reap the attendant benefits of automation. One of the most important optimization tasks is load balancing as it affects network operation right from planning through the lifespan of the network. Several methods for load balancing have been proposed. While some of them have a very buoyant theoretical basis, they are not practically implementable at the current state of technology. Furthermore, most of the techniques proposed employ iterative algorithm, which in itself is not computationally efficient. This paper proposes the use of soft computing, precisely adaptive neuro-fuzzy inference system for dynamic QoS-aware load balancing in 3GPP LTE. Three key performance indicators (i.e. number of satisfied user, virtual load and fairness distribution index) are used to adjust hysteresis task of load balancing

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

Control-data separation architecture for cellular radio access networks: a survey and outlook

Conventional cellular systems are designed to ensure ubiquitous coverage with an always present wireless channel irrespective of the spatial and temporal demand of service. This approach raises several problems due to the tight coupling between network and data access points, as well as the paradigm shift towards data-oriented services, heterogeneous deployments and network densification. A logical separation between control and data planes is seen as a promising solution that could overcome these issues, by providing data services under the umbrella of a coverage layer. This article presents a holistic survey of existing literature on the control-data separation architecture (CDSA) for cellular radio access networks. As a starting point, we discuss the fundamentals, concepts, and general structure of the CDSA. Then, we point out limitations of the conventional architecture in futuristic deployment scenarios. In addition, we present and critically discuss the work that has been done to investigate potential benefits of the CDSA, as well as its technical challenges and enabling technologies. Finally, an overview of standardisation proposals related to this research vision is provided