Data compression by linear prediction for storage and transmission of EEG signals

The EEG time series has been subjected to various formalisms of analysis to extract meaningful information regarding the underlying neural events. In this paper the linear prediction (LP) method has been used for analysis and presentation of spectral array data for the better visualisation of background EEG activity. It has also been used for signal generation, efficient data storage and transmission of EEG. The LP method is compared with the standard Fourier method of compressed spectral array (CSA) of the multichannel EEG data. The autocorrelation autoregressive (AR) technique is used for obtaining the LP coefficients with a model order of 15. While the Fourier method reduces the data only by half, the LP method just requires the storage of signal variance and LP coefficients. The signal generated using white Gaussian noise as the input to the LP filter has a high correlation coefficient of 0.97 with that of original signal, thus making LP as a useful tool for storage and transmission of EEG. The biological significance of Fourier method and the LP method in respect to the microstructure of neuronal events in the generation of EEG is discussed.

Laser patterning system for integrated optics and storage applications

A computer-controlled laser writing system for optical integrated circuits and data storage is described. The system is characterized by holographic (649F) and high-resolution plates. A minimum linewidth of 2.5 mum is obtained by controlling the system parameters. We show that this system can also be used for data storage applications.

A Split-and-Merge Dictionary Learning Algorithm for Sparse Representation: Application to Image Denoising

In big data image/video analytics, we encounter the problem of learning an over-complete dictionary for sparse representation from a large training dataset, which cannot be processed at once because of storage and computational constraints. To tackle the problem of dictionary learning in such scenarios, we propose an algorithm that exploits the inherent clustered structure of the training data and make use of a divide-and-conquer approach. The fundamental idea behind the algorithm is to partition the training dataset into smaller clusters, and learn local dictionaries for each cluster. Subsequently, the local dictionaries are merged to form a global dictionary. Merging is done by solving another dictionary learning problem on the atoms of the locally trained dictionaries. This algorithm is referred to as the split-and-merge algorithm. We show that the proposed algorithm is efficient in its usage of memory and computational complexity, and performs on par with the standard learning strategy, which operates on the entire data at a time. As an application, we consider the problem of image denoising. We present a comparative analysis of our algorithm with the standard learning techniques that use the entire database at a time, in terms of training and denoising performance. We observe that the split-and-merge algorithm results in a remarkable reduction of training time, without significantly affecting the denoising performance.

A Method to Find Sequentially Separated Motifs in Biological Sequences (SSMBS)

Sequence motifs occurring in a particular order in proteins or DNA have been proved to be of biological interest. In this paper, a new method to locate the occurrences of up to five user-defined motifs in a specified order in large proteins and in nucleotide sequence databases is proposed. It has been designed using the concept of quantifiers in regular expressions and linked lists for data storage. The application of this method includes the extraction of relevant consensus regions from biological sequences. This might be useful in clustering of protein families as well as to study the correlation between positions of motifs and their functional sites in DNA sequences.

A hybrid computer for X-ray crystallography

A hybrid computer for structure factor calculations in X-ray crystallography is described. The computer can calculate three-dimensional structure factors of up to 24 atoms in a single run and can generate the scatter functions of well over 100 atoms using Vand et al., or Forsyth and Wells approximations. The computer is essentially a digital computer with analog function generators, thus combining to advantage the economic data storage of digital systems and simple computing circuitry of analog systems. The digital part serially selects the data, computes and feeds the arguments into specially developed high precision digital-analog function generators, the outputs of which being d.c. voltages, are further processed by analog circuits and finally the sequential adder, which employs a novel digital voltmeter circuit, converts them back into digital form and accumulates them in a dekatron counter which displays the final result. The computer is also capable of carrying out 1-, 2-, or 3-dimensional Fourier summation, although in this case, the lack of sufficient storage space for the large number of coefficients involved, is a serious limitation at present.

A modified sulphide capacity function

The sulphide capacity as originally defined by Fincham and Richardson is a strong function of composition in pseudobinary oxide melts of interest in extractive metallurgy. From an analysis of data available in the literature, it is shown that sulphide capacity is directly proportional to the activity of the basic oxide in the melt, within the uncertainty of experimental data. A single parameter is sufficient to describe the sulphide capacity of a binary slag system under isothermal and isobaric conditions. The correlation indicates that the activity coefficient of the sulphide ion or the neutral base metal sulphide dissolved in the melt is independent of composition in pseudobinary melts within experimental uncertainty. Structural variations in the melt with composition do not seem to affect the activity coefficient of the sulphide. A modified sulphide capacity function is defined which makes the treatment more elegant and greatly simplifies data storage and retrieval. The modified function is not based on any model for the melt.

Giant magnetoresistance in transition metal oxides

Some materials exhibit large changes in electrical resistance in the presence of a magnetic field, and this change can be used in applications from sensor technology to magnetic data storage. In their Perspective, Rao and Cheetham discuss magnetoresistance in perovskite manganates, where the effect is unusually strong. Much has been learned about these materials, and this understanding is driving the search for new materials with even more impressive properties.

Signal compression through spatial frequency-based motion estimation

The interest in low bit rate video coding has increased considerably. Despite rapid progress in storage density and digital communication system performance, demand for data-transmission bandwidth and storage capacity continue to exceed the capabilities of available technologies. The growth of data-intensive digital audio, video applications and the increased use of bandwidth-limited media such as video conferencing and full motion video have not only sustained the need for efficient ways to encode analog signals, but made signal compression central to digital communication and data-storage technology. In this paper we explore techniques for compression of image sequences in a manner that optimizes the results for the human receiver. We propose a new motion estimator using two novel block match algorithms which are based on human perception. Simulations with image sequences have shown an improved bit rate while maintaining ''image quality'' when compared to conventional motion estimation techniques using the MAD block match criteria.

Leakage current behavior in pulsed laser deposited Ba(Zr0.05Ti0.95)O3 thin films

Barium zirconium titanate [Ba(Zr0.05Ti0.95)O3, BZT] thin films were prepared by pulsed laser ablation technique and dc leakage current conduction behavior was extensively studied. The dc leakage behavior study is essential, as it leads to degradation of the data storage devices. The current-voltage (I-V) of the thin films showed an Ohmic behavior for the electric field strength lower than 7.5 MV/m. Nonlinearity in the current density-voltage (J-V) behavior has been observed at an electric field above 7.5 MV/m. Different conduction mechanisms have been thought to be responsible for the overall I-V characteristics of BZT thin films. The J-V behavior of BZT thin films was found to follow Lampert’s theory of space charge limited conduction similar to what is observed in an insulator with charge trapping moiety. The Ohmic and trap filled limited regions have been explicitly observed in the J-V curves, where the saturation prevailed after a voltage of 6.5 V referring the onset of a trap-free square region. Two different activation energy values of 1.155 and 0.325 eV corresponding to two different regions have been observed in the Arrhenius plot, which was attributed to two different types of trap levels present in the film, namely, deep and shallow traps.

Low temperature combustion synthesis and magnetostructural properties of Co-Mn nanoferrites

In the present work, Co1-xMnxFe2O4 nanoparticles were synthesized by the low-temperature auto-combustion method. The thermal decomposition process was investigated by means of differential and thermal gravimetric analysis (TG-DTA) that showed the precursor yield the final product above 450 degrees C. The phase purity and crystal lattice symmetry were estimated from X-ray diffraction (XRD). Microstructural features observed by scanning electron microscopy (SEM) demonstrates that the fine clustered particles were formed with an increase in average grain size with Mn2+ content. Fourier transform infrared spectroscopy (FTIR) study confirms the formation of spinel ferrite. Room temperature magnetization measurements showed that the magnetization M-s increases from 29 to 60 emu/g and H-c increases from 13 to 28 Oe with increase in Mn2+ content, which implies that these materials may be applicable for magnetic data storage and recording media. (C) 2013 Elsevier B.V. All rights reserved.

Direct hexagonal transition of amorphous (Ge2Sb2Te5)(0.9)Se-0.1 thin films

Ge2Sb2Te5 (GST) is well known for its phase change properties and applications in memory and data storage. Efforts are being made to improve its thermal stability and transition between amorphous and crystalline phases. Various elements are doped to GST to improve these properties. In this work, Se has been doped to GST to study its effect on phase change properties. Amorphous GST film crystallized in to rock salt (NaCl) type structure at 150 degrees C and then transformed to hexagonal structure at 250 degrees C. Interestingly, Se doped GST ((GST)(0.9)Se-0.1) film crystallized directly into hexagonal phase and the intermediate phase of NaCl is not observed. The crystallization temperature (T-c) of (GST)(0.9)Se-0.1 is around 200 degrees C, which is 50 degrees C higher than the T-c of GST. For (GST)(0.9)Se-0.1, the threshold switching occurs at about 4.5V which is higher than GST (3 V). Band gap (E-opt) values of as deposited films are calculated from Tauc plot which are 0.63 eV for GST and 0.66 eV for (GST)(0.9)Se-0.1. The E-opt decreases for the films annealed at higher temperatures. The increased T-c, E-opt, the contrast in resistance and the direct transition to hexagonal phase may improve the data readability and thermal stability in the Se doped GST film. (C) 2014 AIP Publishing LLC.

Interface-assisted spintronics: Tailoring at the molecular scale

Organic molecules adsorbed on magnetic surfaces offer the possibility to merge the concepts of molecular electronics with spintronics to build future nanoscale data storage, sensing, and computing multifunctional devices. In order to engineer the functionalities of such hybrid spintronic devices, an understanding of the electronic and magnetic properties of the interface between carbon-based aromatic materials and magnetic surfaces is essential. In this article, we discuss recent progress in the study of spin-dependent chemistry and physics associated with the above molecule-ferromagnet interface by combining state-of-the-art experiments and theoretical calculations. The magnetic properties such as molecular magnetic moment, electronic interface spin-polarization, magnetic anisotropy, and magnetic exchange coupling can be specifically tuned by an appropriate choice of the organic material and the magnetic substrate. These reports suggest a gradual shift in research toward an emerging subfield of interface-assisted molecular spintronics.

Holistic Measures for Evaluating Prediction Models in Smart Grids

The performance of prediction models is often based on ``abstract metrics'' that estimate the model's ability to limit residual errors between the observed and predicted values. However, meaningful evaluation and selection of prediction models for end-user domains requires holistic and application-sensitive performance measures. Inspired by energy consumption prediction models used in the emerging ``big data'' domain of Smart Power Grids, we propose a suite of performance measures to rationally compare models along the dimensions of scale independence, reliability, volatility and cost. We include both application independent and dependent measures, the latter parameterized to allow customization by domain experts to fit their scenario. While our measures are generalizable to other domains, we offer an empirical analysis using real energy use data for three Smart Grid applications: planning, customer education and demand response, which are relevant for energy sustainability. Our results underscore the value of the proposed measures to offer a deeper insight into models' behavior and their impact on real applications, which benefit both data mining researchers and practitioners.

PLAStiCC: Predictive Look-Ahead Scheduling for Continuous dataflows on Clouds

Scalable stream processing and continuous dataflow systems are gaining traction with the rise of big data due to the need for processing high velocity data in near real time. Unlike batch processing systems such as MapReduce and workflows, static scheduling strategies fall short for continuous dataflows due to the variations in the input data rates and the need for sustained throughput. The elastic resource provisioning of cloud infrastructure is valuable to meet the changing resource needs of such continuous applications. However, multi-tenant cloud resources introduce yet another dimension of performance variability that impacts the application's throughput. In this paper we propose PLAStiCC, an adaptive scheduling algorithm that balances resource cost and application throughput using a prediction-based lookahead approach. It not only addresses variations in the input data rates but also the underlying cloud infrastructure. In addition, we also propose several simpler static scheduling heuristics that operate in the absence of accurate performance prediction model. These static and adaptive heuristics are evaluated through extensive simulations using performance traces obtained from Amazon AWS IaaS public cloud. Our results show an improvement of up to 20% in the overall profit as compared to the reactive adaptation algorithm.