Improving the performance of convolutional neural network for skin image classification using the response of image analysis filters

Neural Computing and Applications - In this work, we focus in the analysis of dermoscopy images using convolutional neural networks (CNNs). More specifically, we investigate the value of augmenting...     

Improved performance of quality metrics using saliency map and CSF filter for standard coding H264/AVC

Multimedia Tools and Applications - Despite the great evolution in the field of quality metrics, classical tools such as the PSNR remain useful in the field of evolution of video coding research....     

Comparison of the performance improvement for the two novel SOI-tunnel FETs with the lateral dual-gate and triple-gate

Microsystem Technologies - In this paper, we present two silicon-on-insulator tunnel field-effect transistors (SOI-TFETs), referred as a lateral dual-gate TFET and a lateral triple-gate...     

Modernization of the MX-1321 mass-spectrometer: A new information system for GC/MS analysis

A new informatic system for the MX-1321 mass-spectrometer is described. The system includes a specialized station for acquisition and initial selection of data, and a PC AT computer. The main tasks of the system are cyclic scanning of spectra during GC/MS analysis, acquisition and conversion of data in real time to a mass spectrum (nominal mass-amplitude form) and saving data on a hard disc. The system, without any major modification, can be used to update other types of mass-spectrometers or be incorporated into measuring assemblies that feature high-frequency sampling from a few separate channels. Since the system has built-in clocks, it is especially suited to handling devices operating periodically.     

High performance computing methods for the integration and analysis of biomedical data using SAS

From microarrays and next generation sequencing to clinical records, the amount of biomedical data is growing at an exponential rate. Handling and analyzing these large amounts of data demands that computing power and methodologies keep pace. The goal of this paper is to illustrate how high performance computing methods in SAS can be easily implemented without the need of extensive computer programming knowledge or access to supercomputing clusters to help address the challenges posed by large biomedical datasets. We illustrate the utility of database connectivity, pipeline parallelism, multi-core parallel process and distributed processing across multiple machines. Simulation results are presented for parallel and distributed processing. Finally, a discussion of the costs and benefits of such methods compared to traditional HPC supercomputing clusters is given.     

On the performance of combined data flow and control flow systems: Experiments using two iterative algorithms

Improved multiprocessor performance can be attained by combining data flow and control flow concepts. This type of combined architecture is characterized and several examples of previously proposed machines are given. A new model that permits the analysis of such systems is presented and performance measures are defined. This model is then used to analyze the performance of the algorithms under a wide variety of combined systems. The results of these experiments show that partition size is a major factor in the performance of such systems and an optimal size may be found for given system parameters.     

Profiling the potential biomarkers for cell differentiation of pancreatic cancer using iTRAQ and 2-D LC-MS/MS

Pancreatic cancer is a highly lethal disease that is difficult to diagnose at early stage and even more difficult to cure. SW1990 and PANC-1 represent the two cancer cell lines, which are both derived from pancreatic duct, but at different cell differentiation stages. In this study, we applied the iTRAQ-labeling technology and 2-D strong cation exchange/reversed phase liquid chromatography – LC-MS/MS) to profile the secreted proteins of SW1990 and PANC-1 cells in a conditioned cell culture medium. A total of 401 proteins were identified by MS/MS and protein database searching, the percentages of these proteins predicted in the categories of plasma membrane, intracellular and secreted proteins were 29.2, 32.7 and 38.2%, respectively. Fifty six proteins were identified with unknown functions and 19 proteins were quantified with significant level changes between the two cancer cell lines under the specific cell condition with 12 proteins being up-regulated (>1.3-fold change) in PANC-1 (e.g. FLJ31222 protein, 97 kDa protein, type IV collagenase precursor, 38 kDa protein and centaurin) and seven proteins being up-regulated in SW1990 (e.g. fibroblast growth factor receptor substrate 2, putative p150, hypothetical protein LOC 654463 and LOC 55701). The proteins with significant level changes may provide a baseline to investigate mechanisms underlying the differentiation of two cell lines and can be further screened for better protein biomarkers in pancreatic cancer.     

Design,simulation and performance analysis bio-sensors for the detection of cholera and diarrhea using MEMS technology

Microsystem Technologies - This paper presents two different types of micro channels namely one is cylindrical and the other is rectangular which are designed and simulated using FEM tool for the...     

Performance characteristics of an FT MS-based workflow for label-free differential MS analysis of human plasma: standards, reproducibility, targeted feature investigation, and application to a model of controlled myocardial infarction

Proteomics is undergoing a rapid transformation from a qualitative global peptide sequencing discipline into a quantitative, reproducibility-driven practice. Nowhere is this more evident than in the rapidly expanding field of protein biomarker discovery where the general goal is to uncover statistically robust patterns of differential expression between or among subjects/samples representing distinct biological/temporal states. This report presents the analytical characterization of a label-free LC FT-ICR-MS workflow for differential proteomics analysis of human plasma. The key elements discussed include (i) methodologies for performing properly replicated experiments with highly reproducible sample preparation and analysis, including the use of internal standards to quantify variance at different steps in the process, (ii) a new methodology for performing sample re-analysis that uses off-line targeted robotic acquisition of complementary spectral data (e.g. ECD and/or IRMPD) to enhance the identification of differentially expressed peptides/proteins, and (iii) data processing pipelines capable of integrating the automatic statistical analysis of the label-free (LC-) MS signal, together with the intuitive and highly interactive curation and annotation of differential features using the output from standard sequence database search programs. We illustrate the application of the complete sample-to-annotated-differential-peptides (-proteins) workflow by describing the acquisition and analysis of a large multidimensional dataset from patients undergoing a controlled myocardial infarction resulting in an experimental setup in which each patients serve as their own control. Furthermore, we discuss a couple illustrative examples of mid-level proteins observed in this study whose plasma concentrations change consistently within and across patients, in a treatment- and time-dependent fashion.     

Comparison of Landsat 8 OLI and Landsat 7 ETM+ for estimating grassland LAI using model inversion and spectral indices: case study of Mpumalanga,South Africa

The leaf area index (LAI) is the key biophysical indicator used to assess the condition of rangeland. In this study, we investigated the implications of narrow spectral response, high radiometric resolution (12 bits), and higher signal-to-noise ratio of the Landsat 8 Operational Land Imager (OLI) sensor for the estimation of LAI. The Landsat 8 LAI estimates were compared to that of its predecessors, namely Landsat 7 Enhanced Thematic Mapper Plus (ETM+) (8 bits). Furthermore, we compared the radiative transfer model (RTM) and spectral indices approaches for estimating LAI on rangeland systems in South Africa. The RTM was inverted using artificial neural network (ANN) and lookup table (LUT) algorithms. The accuracy of the models was higher for Landsat 8 OLI, where ANN (root mean squared error, RMSE = 0. 13; R² = 0. 89), LUT (RMSE = 0. 25; R² = 0. 50), compared to Landsat 7 ETM+, where ANN (RMSE = 0. 35; R² = 0. 60), LUT (RMSE = 0. 38; R² = 0. 50). Compared to an empirical approach, the RTM provided higher accuracy. In conclusion, Landsat 8 OLI provides an improvement for the estimation of LAI over Landsat 7 ETM+. This is useful for rangeland monitoring.     

ASTER spectral analysis of ultramafic lamprophyres (carbonatites and aillikites) within the Batain Nappe,northeastern margin of Oman: a proposal developed for spectral absorption

We developed a scientific proposal on spectral absorption in remote sensing and a new image-processing method that is purely based on multispectral satellite image spectra to map ultramafic lamprophyre and carbonatite occurrences. The proposed method provides a simple, yet efficient, tool that will help exploration geologists. In this proposal, in which the spectral absorption is applicable to all satellite images obtained in visible, reflected infrared, and thermal infrared spectral wavelength regions, we found that the carbonatites appear white in colour on a greyscale or RGB thermal infrared image obtained in the thermal infrared wavelength region (3–15 μm) due to molecular emission of thermal energy by such carbonate content, particularly the wavelength recorded by the sensor and that the variation of absorption in spectral bands of an outcrop is due to the differences in percentage of carbonate content or the spectral, spatial, radiometric, or temporal resolution of satellite data or the occurrences of carbonatites to incident energy. The results were confirmed by studying the spectral absorption characteristics of carbonatites in selected world occurrences including parts of Batain Nappe, Oman; Fuerteventura (Canary Islands), Spain; Mount Homa, Kenya; Ol Doinyo Lengai, Tanzania; Mount Weld region, (Laverton), Australia, and Phalaborwa region, South Africa, using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Landsat Thematic Mapper (TM) satellite data. A subsequent study of visible near-infrared (VNIR) and shortwave infrared (SWIR) ASTER spectral bands of Early Cretaceous alkaline ultramafic rocks of Batain Nappe, along the northeastern margin of Oman to map for the occurrences of carbonatite and aillikite (ultramafic lamprophyres) dikes and plugs, showed their detection mainly by the diagnostic CO₃ absorption (2.31–2.33 μm) in ASTER SWIR band 8. The results of image interpretations were verified and confirmed in the field and were validated through the study of laboratory analyses. A few more carbonatite dike occurrences were interpreted directly over the greyscale image of ASTER bands and true-colour interpretations of a Google Earth image along this margin. The carbonatites and aillikite occurrences of the area are rich in apatite, iron oxide, phlogopite, and REE-rich minerals and warrant new exploration projects.     

Compressed spectral arrays for the analysis of 24-hr heart rate variability signal: enhancement of parameters and data reduction

Heart rate variability signal in the form of an R-R interval tachogram is detected in Holter type 24-hr ECG recordings. Spectral analysis is carried out over consecutive nonoverlapping records, and the information is displayed in the form of a compressed spectral array through parametric techniques. The trends of spectral parameters such as low-frequency (LF) and high-frequency (HF) powers and central frequencies are also plotted, together with the classical mean R-R value and variance relative to each single spectrum. These parameters quantify the effect of sympatho-vagal balance on heart rate control during the 24-hr period and provide important elements for the diagnostic evaluation of various pathologies, like hypertension. A spectral compression algorithm which checks the position of the poles relative to LF and HF bands inside the unitary circle in the complex zeta-plane is also developed. Applications of this procedure are foreseen in the clinical evaluation of ambulant patients as well as in the study of physical and psychological stress.     

Comparison of two hyperspectral imaging and two laser-induced fluorescence instruments for the detection of zinc stress and chlorophyll concentration in bahia grass (Paspalum notatum Flugge.)

Bahia grass (Paspalum notatum Flugge.) plants were grown in silica sand and irrigated daily with one of five levels of Zn (0, 0.5, 25, 50, or 100 mg l⁻¹) to determine the effects of the heavy metal on the growth and development of plant canopies. Healthy and stressed plants were measured with two hyperspectral imagers, laser-induced fluorescence spectroscopy (LIFS), and laser-induced fluorescence imaging (LIFI) systems in order to determine if the four handheld remote sensing instruments were equally capable of detecting plant stress and measuring canopy chlorophyll levels in bahia grass. Symptoms of bahia grass plants grown at deficient (0 mg l⁻¹) or toxic (25, 50, or 100 mg l⁻¹) concentrations of Zn were dominated by leaf chlorosis and plant stunting. Leaf fresh weight, leaf dry weight, CO₂ assimilation, total chlorophyll, and leaf thickness followed (+) quadratic models in which control plants (0.5 mg l⁻¹ Zn) exhibited higher responses than plants grown at either deficient or toxic levels of Zn. Normalized difference vegetation index [NDVI=(NIR−Red)/(NIR+Red)] and ratio vegetation index [RVI=R₇₅₀/R₇₀₀, in which R denotes reflectance] values were calculated for calibrated digital images from both hyperspectral imagers. The NDVI and RVI values from both hyperspectral imagers were fit best by (+) quadratic models when treatments were constrained between 0 and 100 mg l⁻¹ Zn, but were fit best by linear regression models with (−) slopes when treatments were constrained between 0.5 and 100 mg l⁻¹ Zn. Furthermore, both NDVI and RVI algorithms were effective in predicting the concentrations of chlorophyll in canopies of bahia grass grown at the various levels of Zn. In contrast, red/far-red (R/FR) fluorescence ratios estimated from leaf fluorescence values measured with the LIFS and LIFI instruments were fit best by (−) quadratic models when treatments were constrained between 0 and 100 mg l⁻¹ Zn, but were fit best by linear regression models with (+) slopes when treatments were constrained between 0.5 and 100 mg l⁻¹ Zn. A series of regression analyses were conducted among plant biometric, biochemical, and leaf anatomical parameters (treated as independent variables) and the remote sensing algorithms, NDVI, RVI, blue/green (BL/GR), and R/FR (treated as dependant variables). In general, residuals were significantly higher for NDVI and RVI models compared to the BL/GR and R/FR models indicating that the NDVI and RVI algorithms were able to measure total chlorophyll and plant biomass more accurately than the BL/GR and R/FR algorithms. However, unique capabilities of LIFS and LIFI instruments continue to argue for the development of laser-induced fluorescence remote sensing technologies.     

Frontier-based performance analysis models for supply chain management: State of the art and research directions

Effective supply chain management relies on information integration and implementation of best practice techniques across the chain. Supply chains are examples of complex multi-stage systems with temporal and causal interrelations, operating multi-input and multi-output production and services under utilization of fixed and variable resources. Acknowledging the lack of system’s view, the need to identify system-wide and individual effects as well as incorporating a coherent set of performance metrics, the recent literature reports on an increasing, but yet limited, number of applications of frontier analysis models (e.g. DEA) for the performance assessment of supply chains or networks. The relevant models in this respect are multi-stage models with various assumptions on the intermediate outputs and inputs, enabling the derivation of metrics for technical and cost efficiencies for the system as well as the autonomous links. This paper reviews the state of the art in network DEA modeling, in particular two-stage models, along with a critical review of the advanced applications that are reported in terms of the consistency of the underlying assumptions and the results derived. Consolidating current work in this range using the unified notations and comparison of the properties of the presented models, the paper is closed with recommendations for future research in terms of both theory and application.