Krikor B. Ozanyan

Bio: Krikor B. Ozanyan is an academic researcher from University of Manchester. The author has contributed to research in topics: Iterative reconstruction & Gait (human). The author has an hindex of 21, co-authored 140 publications receiving 1585 citations. Previous affiliations of Krikor B. Ozanyan include Norwegian University of Science and Technology & Sofia University.

Electrons in feldspar II: A consideration of the influence of conduction band-tail states on luminescence processes

Abstract: Most natural feldspars contain many charged impurities, and display a range of bond angles, distributed about the ideal. These effects can lead to complications in the structure of the conduction band, giving rise to a tail of energy states (below the high-mobility conduction band) through which electrons can travel, but with reduced mobility: transport through these states is expected to be thermally activated. The purpose of this article is twofold. Firstly, we consider what kind of lattice perturbations could give rise to both localized and extended conduction band-tail states. Secondly, we consider what influence the band tails have on the luminescence properties of feldspar, where electrons travel through the sample prior to recombination. The work highlights the dominant role that 0.04–0.05-eV phonons play in both the luminescence excitation and emission processes of these materials. It also has relevance in the dating of feldspar sediments at elevated temperatures.

IEEE Sensors Journal

Abstract: Neurophysiology is a specific branch of physiology that deals with neuro-cerebral areas, in particular with the nervous system. The need for monitoring all the nervous system is nowadays, partially possible thanks to the advances in micro and nanotechnologies. Neurophysiology mostly includes topics involving muscles, ears and audiometry, eye (ERG, EOG, visual field) and brain issues. The brain, as the most important organ studied in neurophysiology, attracts interest as subject of wide research activities, e.g. brain imaging new modalities (including X-ray, CT, MRI, SPECT, PET, Biomagnetism, DTI, etc..), BCI, EEG, evoked potentials, cell potentials, neuro-rehabilitation, etc. Micro and nanosensors are contributing hugely to the above research. The same applies to bio-materials for sensors, as well as other polymers, underpinned by advances in materials science and engineering. The special issue intends to promote and to collect original and novel papers that will give significant contribution in the field of sensors and sensing systems dedicated to neurophysiology. Regular papers, tutorials and review papers are sought in neurophysiology areas including (but not limited to):

Human activity recognition with inertial sensors using a deep learning approach

Abstract: Our focus in this research is on the use of deep learning approaches for human activity recognition (HAR) scenario, in which inputs are multichannel time series signals acquired from a set of body-worn inertial sensors and outputs are predefined human activities. Here, we present a feature learning method that deploys convolutional neural networks (CNN) to automate feature learning from the raw inputs in a systematic way. The influence of various important hyper-parameters such as number of convolutional layers and kernel size on the performance of CNN was monitored. Experimental results indicate that CNNs achieved significant speed-up in computing and deciding the final class and marginal improvement in overall classification accuracy compared to the baseline models such as Support Vector Machines and Multi-layer perceptron networks.

Toward in-cylinder absorption tomography in a production engine

Abstract: Design requirements for an 8000 frame/s dual-wavelength ratiometric chemical species tomography system, intended for hydrocarbon vapor imaging in one cylinder of a standard automobile engine, are examined. The design process is guided by spectroscopic measurements on iso-octane and by comprehensive results from laboratory phantoms and research engines, including results on temporal resolution performance. Novel image reconstruction techniques, necessary for this application, are presented. Recent progress toward implementation, including details of the optical access arrangement employed and signal-to-noise issues, is described. We present first cross-cylinder IR absorption measurements from a reduced channel-count (nontomographic) system and discuss the prospects for imaging.

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.