A tighter analysis of the worst-case endto- end communication delay in massive multicores

"Many-core” systems based on the Network-on- Chip (NoC) architecture have brought into the fore-front various opportunities and challenges for the deployment of real-time systems. Such real-time systems need timing guarantees to be fulfilled. Therefore, calculating upper-bounds on the end-to-end communication delay between system components is of primary interest. In this work, we identify the limitations of an existing approach proposed by [1] and propose different techniques to overcome these limitations

Graphene and derivatives – Synthesis techniques, properties and their energy applications

2D nanomaterials with exceptional electrical, mechanical and thermal properties are promising reinforcing materials for fabricating high-performance composite materials. Rapid developments in nanotechnology in recent years have facilitated the development of advanced materials for functional devices. In particular, this review is focussed on the application of graphene nanoparticle-based composites (GNP's) and graphene derivatives in the fields of energy storage and conversion devices. This review focuses on these recent developments including the synthesis of graphene-based materials and its derivative, as well as the related achieved electrical, mechanical and thermal properties

Parametric study for graphene reinforced aluminum matrix composites production using Box Behnken design

The production of graphene reinforced aluminum matrix composite through powder metallurgical route requires optimization of process parameters to obtain better performance characteristics. One of the advanced method available for statistical analysis of parameters is Response Surface Methodology (RSM). The statistical analysis was carried out with three parameters, weight percentage of graphene reinforcement Wg (0.05%, 0.1% and 0.2%), stirring time ST(1h, 2h and 3h) and compaction pressure Pc(16T, 17T and 19T) while sintering temperature T kept constant. The performance of the Box Behnken design was analyzed and optimized using Design Expert software for the effective production of composites. From the results obtained from the analysis, the best set of parameters were considered for the future production of composites

Topology Management in Wireless Sensor Networks: Multi-State Algorithms

In order to maximize the network’s lifetime and ensure the connectivity among the nodes, most topology management practices use a subgroup of nodes for routing. This paper provides an in-depth look at existing topology management control algorithms in Multi-state structure. We suggest a new algorithm based on Geographical Adaptive Fidelity (GAF) and Adaptive Self-Configuring Sensor Networks Topology (ASCENT). The new proposed algorithm outperforms both GAF and ASCENT algorithms

The minimum mean monopoly energy of a graph

The motivation for the study of the graph energy comes from chemistry, where the research on the so-called total pi - electron energy can be traced back until the 1930s. This graph invariant is very closely connected to a chemical quantity known as the total pi - electron energy of conjugated hydro carbon molecules. In recent times analogous energies are being considered, based on Eigen values of a variety of other graph matrices. In 1978, I.Gutman [1] defined energy mathematically for all graphs. Energy of graphs has many mathematical properties which are being investigated. The ordinary energy of an undirected simple finite graph G is defined as the sum of the absolute values of the Eigen values of its associated matrix. i.e. if mu(1), mu(2), ..., mu(n) are the Eigen values of adjacency matrix A(G), then energy of graph is Sigma(G) = Sigma(n)(i=1) vertical bar mu(i)vertical bar Laura Buggy, Amalia Culiuc, Katelyn Mccall and Duyguyen [9] introduced the more general M-energy or Mean Energy of G is then defined as E-M (G) = Sigma(n)(i=1)vertical bar mu(i) - (mu) over bar vertical bar, where (mu) over bar vertical bar is the average of mu(1), mu(2), ..., mu(n). A subset M subset of V (G), in a graph G (V, E), is called a monopoly set of G if every vertex v is an element of (V - M) has at least d(v)/2 neighbors in M. The minimum cardinality of a monopoly set among all monopoly sets in G is called the monopoly size of G, denoted by mo(G) Ahmed Mohammed Naji and N.D.Soner [7] introduced minimum monopoly energy E-MM [G] of a graph G. In this paper we are introducing the minimum mean monopoly energy, denoted by E-MM(M) (G), of a graph G and computed minimum monopoly energies of some standard graphs. Upper and lower bounds for E-MM(M) (G)are also established.Publisher's Versio

Calibration of second-order correlation functions for non-stationary sources with a multi-start multi-stop time-to-digital converter

A novel high-throughput second-order-correlation measurement system is developed which records and makes use of all the arrival times of photons detected at both start and stop detectors. This system is suitable particularly for a light source having a high photon flux and a long coherence time since it is more efficient than conventional methods by an amount equal to the product of the count rate and the correlation time of the light source. We have used this system in carefully investigating the dead time effects of detectors and photon counters on the second-order correlation function in the two-detector configuration. For a non-stationary light source, distortion of original signal was observed at high photon flux. A systematic way of calibrating the second-order correlation function has been devised by introducing a concept of an effective dead time of the entire measurement system.Comment: 7 pages, 6 figure

Mechanical properties of graphene oxide reinforced aluminium matrix composites

In this paper, the properties of powder metallurgy produced samples of GO reinforced aluminium composites were examined. Discs of 20 mm diameter and 0.5 mm thickness were made from pure Al powder of 35 μm particle size and with GO reinforcement at different GO wt% (0.05, 0.1 and 0.2). The mixture of Al/GO powders prepared through liquid infiltration were cold compacted and then sintered. The GO reinforced Al matrix composites were characterised using the scanning e lectron microscope with energy dispersion spectroscopy (SEM/ EDX ) for investigation of the homogeneous dispersion of GO into the matrix. X - ray diffraction (XRD) analysis for crystallographic phase and micro - Raman spectroscopy w as used to identify the phases inside the composite matrix after the sintering process. Micro hardness and the strength values from the produced Al/GO composites were recorded. It is evident from the results obtained that where uniform mixing is achieved, GO reinforced Al composites ca n be produced with similar hardness values as for those produced from rGO reinforced Al composites