Gradient-based value mapping for pseudocolor images

We develop a method for automatic colorization of images (or two-dimensional fields) in order to visualize pixel values and their local differences. In many applications, local differences in pixel values are as important as their values. For example, in topography, both elevation, and slope often must be considered. Gradient-based value mapping (GBVM) is a technique for colorizing pixels based on value (e.g., intensity or elevation) and gradient (e.g., local differences or slope). The method maps pixel values to a color scale (either gray-scale or pseudocolor) in a manner that emphasizes gradients in the image while maintaining ordinal relationships of values. GBVM is especially useful for high-precision data, in which the number of possible values is large. Colorization with GBVM is demonstrated with data from comprehensive two-dimensional gas chromatography (GCxGC), using both gray-scale and pseudocolor to visualize both small and large peaks, and with data from the Global Land One-Kilometer Base Elevation (GLOBE) Project, using grayscale to visualize features that are not visible in images produced with popular value-mapping algorithms

Gradient-based value mapping for pseudocolor images

We develop a method for automatic colorization of images (or two-dimensional fields) in order to visualize pixel values and their local differences. In many applications, local differences in pixel values are as important as their values. For example, in topography, both elevation and slope often must be considered. Gradient based value mapping (GBVM) is a technique for colorizing pixels based on value (e.g., intensity or elevation) and gradient (e.g., local differences or slope). The method maps pixel values to a color scale (either gray-scale or pseudocolor) in a manner that emphasizes gradients in the image while maintaining ordinal relationships of values. GBVM is especially useful for high-precision data, in which the number of possible values is large. Colorization with GBVM is demonstrated with data from comprehensive two-dimensional gas chromatography (GCxGC), using both gray-scale and pseudocolor to visualize both small and large peaks, and with data from the Global Land One-Kilometer Base Elevation (GLOBE) Project, using gray scale to visualize features that are not visible in images produced with popular value-mapping algorithms

Information-theoretic mass spectral library search for comprehensive two-dimensional gas chromatography with mass spectrometry

Comprehensive Two-Dimensional Gas Chromatography with Mass Spectrometry (GCxGC-MS) combines two techniques providing increased separation capacity and enhanced capability for chemical identification. One of the most important methods for chemical identification is library search, which searches for an unknown mass spectrum in a library of known mass spectra to produce a list of potential matches ordered by match quality. Applications of compound identification include environmental monitoring, forensics, security, food and medicine. This dissertation presents a new information-theoretic mass spectral library search technique for compound identification in GCxGC-MS and other MS applications. The method is based on a similarity measure between an unknown spectrum and a library spectrum involving the probability distribution functions of the intensities in the library and the noise in the data. The new method characterizes the library with an array of probability distribution functions of intensities as a function of mass-to-charge ratio. Each probability in the distribution function characterizes the fraction of spectra in the library having that intensity value at the given mass-to-charge ratio. The instrument noise is modelled with parameters estimated by statistically analyzing within individual GCxGC-MS peaks the intensity variations at each mass-to-charge ratio. Experimental results demonstrate the effectiveness and robustness of the new information-theoretic mass spectral library search technique. In simulation experiments, random spectra from the NIST/EPA/NIH Mass Spectral Library were corrupted with synthetic noise to generate random test spectra. Then, the corrupted spectra were submitted as unknowns for the library search using different search techniques. Experiments evaluated search performance with additive signal-independent noise, signal-dependent noise, (Johnson) colored noise, and spectral noise (from another spectrum selected randomly from the library). Other experiments evaluated search performance for real GCxGC-MS data. Search techniques were evaluated for many trials under each experimental condition by the Average Rank of the correct match in the ordered list of potential matches returned by the respective search techniques. The new information-theoretic mass spectral library search technique performs better than NIST MS Search and Probability Based Matching (PBM) for all noise models; that is, the new search technique ranked the correct spectrum higher in the ordered list of potential matches than NIST MS Search and PBM for all noise models. In experiments with real data from GCxGC-Time-of-Flight-MS instruments and GCxGC-Quadrupole-MS instruments, the noise parameters were estimated by statistical analysis of mass spectral variations in multiple spectra of GCxGC-MS peaks and the weighted mean spectra of the peaks (added to the library as the correct match). In the experiments with real data, the information-theoretic mass spectral library search technique worked better than NIST MS Search and PBM in most cases. Keywords. information theory, library search, compound identification, mass spectrum, noise model, similarity measure

Voltage and Temperature-Aware SSTA Using Neural Network Delay Model

With the emergence of voltage scaling as one of the most powerful power reduction techniques, it has been important to support voltage scalable statistical static timing analysis (SSTA) in deep submicrometer process nodes. In this paper, we propose a single delay model of logic gate using neural network which comprehensively captures process, voltage, and temperature variation along with input slew and output load. The number of simulation programs with integrated circuit emphasis (SPICE) required to create this model over a large voltage and temperature range is found to be modest and 4x less than that required for a conventional table-based approach with comparable accuracy. We show how the model can be used to derive sensitivities required for linear SSTA for an arbitrary voltage and temperature. Our experimentation on ISCAS 85 benchmarks across a voltage range of 0.9-1.1V shows that the average error in mean delay is less than 1.08% and average error in standard deviation is less than 2.85%. The errors in predicting the 99% and 1% probability point are 1.31% and 1%, respectively, with respect to SPICE. The two potential applications of voltage-aware SSTA have been presented, i.e., one for improving the accuracy of timing analysis by considering instance-specific voltage drops in power grids and the other for determining optimum supply voltage for target yield for dynamic voltage scaling applications

Within-Die Gate Delay Variability Measurement Using Reconfigurable Ring Oscillator

We report the design and characterization of a circuit technique to measure the on-chip delay of an individual logic gate (both inverting and noninverting) in its unmodified form. The test circuit comprises of digitally reconfigurable ring oscillator (RO). The gate under test is embedded in each stage of the ring oscillator. A system of linear equations is then formed with different configuration settings of the RO, relating the individual gate delay to the measured period of the RO, whose solution gives the delay of the individual gates. Experimental results from a test chip in 65-nm process node show the feasibility of measuring the delay of an individual inverter to within 1 ps accuracy. Delay measurements of different nominally identicall inverters in close physical proximity show variations of up to 28% indicating the large impact of local variations. As a demonstration of this technique, we have studied delay variation with poly-pitch, length of diffusion (LOD) and different orientations of layout in silicon. The proposed technique is quite suitable for early process characterization, monitoring mature process in manufacturing and correlating model-to-hardware

Within-Die Gate Delay Variability Measurement using Re-configurable Ring Oscillator

We report a circuit technique to measure the on-chip delay of an individual logic gate (both inverting and non-inverting) in its unmodified form using digitally reconfigurable ring oscillator (RO). Solving a system of linear equations with different configuration setting of the RO gives delay of an individual gate. Experimental results from a test chip in 65nm process node show the feasibility of measuring the delay of an individual inverter to within 1pS accuracy. Delay measurements of different nominally identical inverters in close physical proximity show variations of up to 26% indicating the large impact of local or within-die variations