Catalysis by Amberlite IR-120 resin: a rapid and green method for the synthesis of phenols from arylboronic acids under metal, ligand, and base-free conditions

A clean process has been developed for the ipso-hydroxylation of aryl and heteroaryl boronic acids to the corresponding phenols using commercially available and recyclable Amberlite IR-120 resin and aqueous hydrogen peroxide as an oxidizing agent. The ion-exchange sulfonic acid resin catalyst could be readily recycled by filtration and directly reused at least four times without any significant loss of activity.     

A Distributed and Elastic Application Processing Model for Mobile Cloud Computing

The latest developments in mobile computing technology have increased the computing capabilities of smart mobile devices (SMDs). However, SMDs are still constrained by low bandwidth, processing potential, storage capacity, and battery lifetime. To overcome these problems, the rich resources and powerful computational cloud is tapped for enabling intensive applications on SMDs. In Mobile Cloud Computing (MCC), application processing services of computational clouds are leveraged for alleviating resource limitations in SMDs. The particular deficiency of distributed architecture and runtime partitioning of the elastic mobile application are the challenging aspects of current offloading models. To address these issues of traditional models for computational offloading in MCC, this paper proposes a novel distributed and elastic applications processing (DEAP) model for intensive applications in MCC. We present an analytical model to evaluate the proposed DEAP model, and test a prototype application in the real MCC environment to demonstrate the usefulness of DEAP model. Computational offloading using the DEAP model minimizes resources utilization on SMD in the distributed processing of intensive mobile applications. Evaluation indicates a reduction of 74.6% in the overhead of runtime application partitioning and a 66.6% reduction in the CPU utilization for the execution of the application on SMD.

     

Wavelet-modified fringe-adjusted joint transform correlator

In this paper, we implement a wavelet-modified fringe-adjusted joint transform correlator (JTC) for real-time target recognition applications. In real-time situation the input scene is captured using a charge-coupled device (CCD) camera. The obtained joint power spectrum is multiplied by a pre-synthesized fringe-adjusted filter and the resultant function is processed with an appropriately scaled wavelet filter. Three performance measure parameters: correlation peak intensity, peak-to-sidelobe ratio, and signal-to-clutter ratio have been calculated for fringe-adjusted joint transform correlator (FJTC) and wavelet-modified fringe-adjusted joint transform correlator (WFJTC). The WFJTC has been found to yield better results in comparison to conventional FJTC. To suppress the undesired strong dc, the resultant function is differentiated. Differential processing wavelet-modified fringe-adjusted joint power spectrum removes the zero-order spectra and hence improves the detection efficiency. To focus the correlation terms in different planes in order to capture one of the desired autocorrelation peaks and discard the strong dc and another autocorrelation peak, chirp-encoding technique has also been applied. Targets with Gaussian and speckle noise have also been used to check the correlation outputs. Computer simulation and experimental results are presented.     

Communication scheduling to minimize thermal effects of implanted biosensor networks in homogeneous tissue

A network of biosensors can be implanted in a human body for health monitoring, diagnostics, or as a prosthetic device. Biosensors can be organized into clusters where most of the communication takes place within the clusters, and long range transmissions to the base station are performed by the cluster leader to reduce the energy cost. In some applications, the tissues are sensitive to temperature increase and may be damaged by the heat resulting from normal operations and the recharging of sensor nodes. Our work is the first to consider rotating the cluster leadership to minimize the heating effects on human tissues. We explore the factors that lead to temperature increase, and the process for calculating the specific absorption rate (SAR) and temperature increase of implanted biosensors by using the finite-difference time-domain (FDTD) method. We improve performance by rotating the cluster leader based on the leadership history and the sensor locations. We propose a simplified scheme, temperature increase potential, to efficiently predict the temperature increase in tissues surrounding implanted sensors. Finally, a genetic algorithm is proposed to exploit the search for an optimal temperature increase sequence.     

On switching behavior of ferrite substrate with magnetostatic and spin wave exchange term

Due to the generation of magnetostatic and spin waves, switching phenomena of ferrite substrate with a normal magnetic biasing field is presented. Generally below the X-band of microwave range, the Pozar’s quasi TEM waves (extraordinary waves) were studied, but for the study of X-band there should be an inclusion of spin wave exchange term (ωr) in the magnetostatic wave analysis which depends upon the static internal field (Hex). This term is included in analysis because the wavelength of microwave approach is the inter-atomic distance of ferrite material. In this work we synthesize LiTi ferrite through Solid State Reaction Technique (SSRT) and obtained electric and magnetic properties for the analysis. Absorbing and transmission power coefficients have been calculated to obtain the power loss and transmitted power through the substrate, respectively. The absorbing power coefficient verifies the switching behavior of substrate for certain range of applied external magnetic field (H _o) which depends on the resonance line width parameter (ΔH) of ferrite material.