Advancement in Color Image Processing using Geometric Algebra

This paper describes an advancement in color image processing, using geometric algebra. This is achieved using a compact representation of vectors within $n$ dimensional space. Geometric Algebra (GA) is a preferred framework for signal representation and image representation. In this context the R, G, B color channels are not defined separately but as a single entity. As GA provides a rich set of operations, the signal and image processing operations becomes straightforward and the algorithms intuitive. From the experiments described in this paper, it is also possible to conclude that the convolution operation with the rotor masks within GA belong to a class of linear vector filters and can be applied to image or speech signals. The usefulness of the introduced approach has been demonstrated by analyzing and implementing two different types of edge detection schemes

On-chip timing measurement architecture with femtosecond resolution

A new timing measurement architecture based on the time-to-digital conversion technique is presented. The architecture occupies a small silicon area (200x185µm) in a 0.12µm CMOS Process and can achieve tens of femtoseconds timing resolution, which is the highest reported to date

Factors influencing consumer retention of mobile apps : a conceptual perspective on the high-street retails

The high adoption rates of branded mobile applications (Apps) demonstrates its popularity, but also shows that consumers are emerging into an era where products and services are consumed anytime anywhere. Despite the high adoption rates of branded mobile apps, recent ongoing studies highlight the importance of investigating the low retention rates of smartphone mobile apps by consumers. This study presents a conceptual model, which includes factors that motivate consumers to retain mobile apps from highstreet retailers, based on the literature and 21 in-depth interviews with non-student consumers

Functional and anatomic correlates of two frequently observed temporal lobe seizure-onset patterns.

Intracranial depth electrode EEG records of 478 seizures, recorded in 68 patients undergoing diagnostic monitoring with depth electrodes, were evaluated to investigate the correlates of electrographic onset patterns in patients with temporal lobe seizures. The seizure onsets in 78% of these patients were identified as either hypersynchronous onsets, beginning with low-frequency, high-amplitude spikes, or low-voltage fast (LVF) onsets, increasing in amplitude as the seizure progressed. The number of patients (35) having hypersynchronous seizure onsets was nearly twice that of patients (18) having LVF onsets. Three major differences were seen among patients with the two seizure-onset patterns. When compared with patients having LVF onsets, patients with hypersynchronous seizure onsets had a significantly greater probability of having (1) focal rather than regional seizure onsets (p < 0.01), (2) seizures spreading more slowly to the contralateral mesial temporal lobe (p < 0.003), and (3) cell counts in resected hippocampal tissue showing greater neuronal loss (p < 0.001). The results provide evidence that the most frequent electrographic abnormality associated with mesial temporal seizures is local hypersynchrony, a condition associated with major neuronal loss in the hippocampus. The results also indicate that LVF seizure onsets more frequently represent widely distributed discharges, which interact with and spread more rapidly to surrounding neocortical areas

N-acetylcysteine (NAC) ameliorates Epstein-Barr virus latent membrane protein 1 induced chronic inflammation

Chronic inflammation results when the immune system responds to trauma, injury or infection and the response is not resolved. It can lead to tissue damage and dysfunction and in some cases predispose to cancer. Some viruses (including Epstein-Barr virus (EBV)) can induce inflammation, which may persist even after the infection has been controlled or cleared. The damage caused by inflammation, can itself act to perpetuate the inflammatory response. The latent membrane protein 1 (LMP1) of EBV is a pro-inflammatory factor and in the skin of transgenic mice causes a phenotype of hyperplasia with chronic inflammation of increasing severity, which can progress to pre-malignant and malignant lesions. LMP1 signalling leads to persistent deregulated expression of multiple proteins throughout the mouse life span, including TGFα S100A9 and chitinase-like proteins. Additionally, as the inflammation increases, numerous chemokines and cytokines are produced which promulgate the inflammation. Deposition of IgM, IgG, IgA and IgE and complement activation form part of this process and through genetic deletion of CD40, we show that this contributes to the more tissue-destructive aspects of the phenotype. Treatment of the mice with N-acetylcysteine (NAC), an antioxidant which feeds into the body’s natural redox regulatory system through glutathione synthesis, resulted in a significantly reduced leukocyte infiltrate in the inflamed tissue, amelioration of the pathological features and delay in the inflammatory signature measured by in vivo imaging. Reducing the degree of inflammation achieved through NAC treatment, had the knock on effect of reducing leukocyte recruitment to the inflamed site, thereby slowing the progression of the pathology. These data support the idea that NAC could be considered as a treatment to alleviate chronic inflammatory pathologies, including post-viral disease. Additionally, the model described can be used to effectively monitor and accurately measure therapies for chronic inflammation

Theoretical studies of the phase transition in the anisotropic 2-D square spin lattice

The phase transition occurring in a square 2-D spin lattice governed by an anisotropic Heisenberg Hamiltonian has been studied according to two recently proposed methods. The first one, the Dressed Cluster Method, provides excellent evaluations of the cohesive energy, the discontinuity of its derivative around the critical (isotropic) value of the anisotropy parameter confirms the first-order character of the phase transition. Nevertheless the method introduces two distinct reference functions (either N\'eel or XY) which may in principle force the discontinuity. The Real Space Renormalization Group with Effective Interactions does not reach the same numerical accuracy but it does not introduce a reference function and the phase transition appears qualitatively as due to the existence of two domains, with specific fixed points. The method confirms the dependence of the spin gap on the anisotropy parameter occurring in the Heisenberg-Ising domain

Estimation of subsurface porosities and thermal conductivities of polygonal tundra by coupled inversion of electrical resistivity, temperature, and moisture content data

Studies indicate greenhouse gas emissions following permafrost thaw will amplify current rates of atmospheric warming, a process referred to as the permafrost carbon feedback. However, large uncertainties exist regarding the timing and magnitude of the permafrost carbon feedback, in part due to uncertainties associated with subsurface permafrost parameterization and structure. Development of robust parameter estimation methods for permafrost-rich soils is becoming urgent under accelerated warming of the Arctic. Improved parameterization of the subsurface properties in land system models would lead to improved predictions and a reduction of modeling uncertainty. In this work we set the groundwork for future parameter estimation (PE) studies by developing and evaluating a joint PE algorithm that estimates soil porosities and thermal conductivities from time series of soil temperature and moisture measurements and discrete in-time electrical resistivity measurements. The algorithm utilizes the Model-Independent Parameter Estimation and Uncertainty Analysis toolbox and coupled hydrological-thermal-geophysical modeling. We test the PE algorithm against synthetic data, providing a proof of concept for the approach. We use specified subsurface porosities and thermal conductivities and coupled models to set up a synthetic state, perturb the parameters, and then verify that our PE method is able to recover the parameters and synthetic state. To evaluate the accuracy and robustness of the approach we perform multiple tests for a perturbed set of initial starting parameter combinations. In addition, we varied types and quantities of data to better understand the optimal dataset needed to improve the PE method. The results of the PE tests suggest that using multiple types of data improve the overall robustness of the method. Our numerical experiments indicate that special care needs to be taken during the field experiment setup so that (1) the vertical distance between adjacent measurement sensors allows the signal variability in space to be resolved and (2) the longer time interval between resistivity snapshots allows signal variability in time to be resolved