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The aim of this work is to present a complex experimental and theoretical study of the ferromagnetic resonance frequencies and plasma behavior for CoFe-based, Co-based, Fe-based and FINEMET TM glass covered amorphous microwires (GCAWs), arranged in parallel configurations as free-standing systems, as well as combinations thereof, in order to obtain metastructures with tailored and enlarged left-handed frequency domain. The negative magnetic properties interval of the metastructures is expanded by alternating different types of GCAWs with close natural ferromagnetic resonance (NFMR) and natural antiferromagnetic resonance (NFMAR) domains, respectively.

Measurements by impedance spectroscopy and Bruggeman effective medium approximation model were employed in order to determine the mineral volume fraction of dry bone. This approach assumes that two or more phases are present into the composite: the matrix (environment) and the other ones are inclusion phases. A fragment of femur diaphysis dense bone from a young pig was investigated in its dehydrated state. Measuring the dielectric properties of bone and its main components (hydroxyapatite and collagen) and using the Bruggeman approach, the mineral volume filling factor was determined. The computed volume fraction of the mineral volume fraction was confirmed by a histogram test analysis based on the SEM microstructures. In spite of its simplicity, the method provides a good approximation for the bone mineral volume fraction. The method which uses impedance spectroscopy and EMA modeling can be further developed by considering the conductive components of the bone tissue as a non-invasive in situ impedance technique for bone composition evaluation and monitoring.

In the development of magneto-resistance random access memory (MRAM), the theoretical studies are now concentrated in various parameters improving. Some important parameters are the range of the operating field and the switching time. The analysis presented in this paper is concentrated to a synthetic antiferromagnet (SAF). Recently a considerable effort has been expended by Fujiwara and co-workers to the analysis of the switching mode suggested by Savtchenko et al., called also toggle MRAM, and a number of conditions were found to maximize the operating field margin; most of these studies were based on a critical-curve approach. For uniaxial anisotropy, an analytical approach is possible and simple results and critical curves are presented. For the more general case, only the micromagnetic simulation can offer results. The studies are made for different coupling constants between the two ferromagnetic layers and the results are discussed.

A graph is perfect if for each of its induced subgraphs H, the chromatic number χ(H) of H equals the largest number ω(H) or pairwise adjacent vertices in H. We present a new property of minimal imperfect graphs which implies Chvatals well-known Star-Cutset Lemma, and which gives a new characterization of perfect graphs.

Phenomenal advances in nano-technology and packaging have made it possible to develop miniaturized low-power devices that integrate sensing, special-purpose computing, and wireless communications capabilities. It is expected that these small devices, referred to as sensors, will be mass-produced and deployed, making their production cost negligible. Due to their small form factor and modest non-renewable energy budget, individual sensors are not expected to be GPS-enabled. Moreover, in most applications, exact geographic location is not necessary, and all that the individual sensors need is a coarse-grain location awareness. The task of acquiring such a coarse-grain location awareness is referred to as training. In this paper, two scalable energy-efficient training protocols are proposed for massively-deployed sensor networks, where sensors are initially anonymous and unaware of their location. The training protocols are lightweight and simple to implement; they are based on an intuitive coordinate system imposed onto the deployment area which partitions the anonymous sensors into clusters where data can be gathered from the environment and synthesized under local control.