State-of-the-art graphene high-frequency electronics

High-performance graphene transistors for radio frequency applications have received much attention and significant progress has been achieved. However, devices based on large-area synthetic graphene, which have direct technological relevance, are still typically outperformed by those based on mechanically exfoliated graphene. Here, we report devices with intrinsic cutoff frequency above 300 GHz, based on both wafer-scale CVD grown graphene and epitaxial graphene on SiC, thus surpassing previous records on any graphene material. We also demonstrate devices with optimized architecture exhibiting voltage and power gains reaching 20 dB and a wafer-scale integrated graphene amplifier circuit with voltage amplification.     

Emerging,Self-Management Functionality for Introducing Cognition in the Wireless,B3G World

The unparalleled evolution of wireless communications is reflected in the tremendous investments on research and development, targeted at the continuous introduction of innovations that could serve the information society. This has led to the coexistence and complementary exploitation of versatile, legacy and also emerging Radio Access Technologies (RATs). At the same time, the continuously varying environment/users requirements impose the adaptation of those technologies to external stimuli, through reconfiguration (reconsideration) of their infrastructure and/or operating parameters. One feasible option to tackle the increased complexity of such environments, is to design wireless infrastructures with learning capabilities, thus forming cognitive networks. Cognitive networks are able to retain information from their interactions with the environment and intelligently adapt to any requirements. A prerequisite to facilitate operability of cognitive networks is the development of novel management mechanisms, which need to, distributively (centralized approaches would get even more complex), evaluate changes in external conditions and determine the way in which the network will properly respond to them. To this effect, this paper presents a complete framework under which Cognitive Access Points (CgAPs) could be managed and analyzes the functionality of its entities. Moreover, it also provides an approach for managing Cognitive Wireless Network Segments (CgWNSs).

A stochastic optimization formulation for the transition from open pit to underground mining

As open pit mining of a mineral deposit deepens, the cost of extraction may increase up to a threshold where transitioning to mining through underground methods is more profitable. This paper provides an approach to determine an optimal depth at which a mine should transition from open pit to underground mining, based on managing technical risk. The value of a set of candidate transition depths is calculated by optimizing the production schedules for each depth’s unique open pit and underground operations which provide yearly discounted cash flow projections. By considering the sum of the open pit and underground mining portion’s value, the most profitable candidate transition depth is identified. The optimization model presented is based on a stochastic integer program that integrates geological uncertainty and manages technical risk. The proposed approach is tested on a gold deposit. Results show the benefits of managing geological uncertainty in long-term strategic decision-making frameworks. Additionally, the stochastic result produces a 9% net present value increase over a similar deterministic formulation. The risk-managing stochastic framework also produces operational schedules that reduce a mining project`s susceptibility to geological risk. This work aims to approve on previous attempts to solve this problem by jointly considering geological uncertainty and describing the optimal transition depth effectively in 3-dimensions.     

A management scheme for improving transportation efficiency and contributing to the enhancement of the social fabric

The continuously increasing need for mobility has brought about not only significant facilities in several aspects of human initiative, but also growing traffic congestions, a phenomenon that leads to unpleasant everyday situations at a short time level, but in the long run also to the degradation of the level of quality of living in large cities and the alienation between people. The management of traffic stands, thus, as a fundamental prerequisite for confronting those issues, enhancing transportation and improving the social fabric. This paper considers the concept of car pooling as a structured approach to this problem, by specifying, developing and validating a mobile-community-driven system for collaborative transportation, namely the “transportation management-car pooling system”. This system is capable of proposing optimal, reliable and secure community matches (taking into consideration personality features, talking interests, driving style, etc.), based on user profile and context information. The paper describes the transportation management-car pooling system, presenting its input parameters, decision making process and outcomes. Finally, indicative simulation results showcase its effectiveness.     

Low-Power Leading-Zero Counting and Anticipation Logic for High-Speed Floating Point Units

In this paper, a new leading-zero counter (or detector) is presented. New boolean relations for the bits of the leading-zero count are derived that allow their computation to be performed using standard carry-lookahead techniques. Using the proposed approach various design choices can be explored and different circuit topologies can be derived for the design of the leading-zero counting unit. The new circuits can be efficiently implemented either in static or in dynamic logic and require significantly less energy per operation compared to the already known architectures. The integration of the proposed leading-zero counter with the leading-zero anticipation logic is analyzed and the most efficient combination is identified. Finally, a simple yet efficient technique for handling the error of the leading-zero anticipation logic is also presented. The energy-delay behavior of the proposed circuits has been investigated using static and dynamic CMOS implementations in a 130-nm CMOS technology.     

Using Remote Sensing to Assess Impact of Mining Activities on Land and Water Resources

Environmental impact assessment is now an integral part of mining operations. Remote sensing data enables the identification, delineating, and monitoring of pollution sources and affected areas, including derelict land, and changes in surface land use and to water bodies. The aim of this work was to evaluate the use of multi-temporal Landsat-5 and Landsat-7 images, SPOT Panchromatic, and ASTER data to map the natural environment on a local scale, and to assess the impact of mining activities by indicating the changes on land and water resources. Three case studies are presented: Lake Vegoritis and the Amynteon mine (both located in northern Greece) and the Lavrio mine area, in central Greece. We found that using high resolution satellite remote sensing data and state of the art GIS techniques with parallel development of a fully integrated geospatial database system provided monitoring and feedback at appropriate spatial scales; therefore, such data can be used for long-term environmental management and monitoring of reclamation and rehabilitation of mining areas.     

Incorporating geological and market uncertainties and operational flexibility into open pit mine design

This work outlines a procedure for integrating uncertainty and operational flexibility into open pit mine design selection. A multi-criteria design ranking system based on advanced uncertainty and financial modeling techniques such as Monte Carlo simulation and real options is proposed. A case study at a copper mine is provided.     

An Improved Computational Method for the Calculation of Mixture Liquid–Vapor Critical Points

Knowledge of critical points is important to determine the phase behavior of a mixture. This work proposes a reliable and accurate method in order to locate the liquid–vapor critical point of a given mixture. The theoretical model is developed from the rigorous definition of critical points, based on the SRK equation of state (SRK EoS) or alternatively, on the PR EoS. In order to solve the resulting system of \(C+2\) nonlinear equations, an improved method is introduced into an existing Newton–Raphson algorithm, which can calculate all the variables simultaneously in each iteration step. The improvements mainly focus on the derivatives of the Jacobian matrix, on the convergence criteria, and on the damping coefficient. As a result, all equations and related conditions required for the computation of the scheme are illustrated in this paper. Finally, experimental data for the critical points of 44 mixtures are adopted in order to validate the method. For the SRK EoS, average absolute errors of the predicted critical-pressure and critical-temperature values are 123.82 kPa and 3.11 K, respectively, whereas the commercial software package Calsep PVTSIM’s prediction errors are 131.02 kPa and 3.24 K. For the PR EoS, the two above mentioned average absolute errors are 129.32 kPa and 2.45 K, while the PVTSIM’s errors are 137.24 kPa and 2.55 K, respectively.