Containing the Nanometer "Pandora-Box": Cross-Layer Design Techniques for Variation Aware Low Power Systems

The demand for richer multimedia services, multifunctional portable devices and high data rates can only been visioned due to the improvement in semiconductor technology. Unfortunately, sub-90 nm process nodes uncover the nanometer Pandora-box exposing the barriers of technology scaling-parameter variations, that threaten the correct operation of circuits, and increased energy consumption, that limits the operational lifetime of today's systems. The contradictory design requirements for low-power and system robustness, is one of the most challenging design problems of today. The design efforts are further complicated due to the heterogeneous types of designs ( logic, memory, mixed-signal) that are included in today's complex systems and are characterized by different design requirements. This paper presents an overview of techniques at various levels of design abstraction that lead to low power and variation aware logic, memory and mixed-signal circuits and can potentially assist in meeting the strict power budgets and yield/quality requirements of future systems.

The feasibility of using near infrared and Raman spectroscopic techniques to detect fraudulent adulteration of chili powders with Sudan dye

Chili powder is a globally traded commodity which has been found to be adulterated with Sudan dyes from 2003 onwards. In this study, chili powders were adulterated with varying quantities of Sudan I dye (0.1-5%) and spectra were generated using near infrared reflectance spectroscopy (NIRS) and Raman
spectroscopy (on a spectrometer with a sample compartment modified as part of the study). Chemometrics were applied to the spectral data to produce quantitative and qualitative calibration models and prediction statistics. For the quantitative models coefficients of determination (R2) were found to be
0.891-0.994 depending on which spectral data (NIRS/Raman) was processed, the mathematical algorithm used and the data pre-processing applied. The corresponding values for the root mean square error of calibration (RMSEC) and root mean square error of prediction (RMSEP) were found to be 0.208-0.851%
and 0.141-0.831% respectively, once again depending on the spectral data and the chemometric treatment applied to the data. Indications are that the NIR spectroscopy based models are superior to the models produced from Raman spectral data based on a comparison of the values of the chemometric
parameters. The limit of detection (LOD) based on analysis of 20 blank chili powders against each calibration model gave 0.25% and 0.88% for the NIR and Raman data, respectively. In addition, adopting a qualitative approach with the spectral data and applying PCA or PLS-DA, it was possible to discriminate
between adulterated chili powders from non-adulterated chili powders.

Two-dimensional images of dissolved sulfide and metals in anoxic sediments by a novel diffusive gradients in thin film probe and optical scanning techniques

A novel diffusive gradients in thin film probe developed comprises diffusive gel layer of silver iodide (AgI) and a back-up Microchelex resin gel layer. 2D high-resolution images of sulfide and trace metals were determined respectively on the AgI gel by densitometric analysis and on the Microchelex resin layer with laser-ablation-inductively-coupled plasma mass spectrometry (LA-ICP-MS).We investigated the validity of the analytical procedures used for the determination of sulfide and trace metals. We found low relative standard deviations on replicate measurements, linear trace-metal calibration curves between the LA-ICP-MS signal and the true trace-metal concentration in the resin gel, and a good agreement of the sulfide results obtained with the AgI resin gel and with other analytical methods. The method was applied on anoxic sediment pore waters in an estuarine and marine system. Simultaneous remobilization of sulfide and trace metals was observed in the marine sediment.

Identification of vegetable oil botanical speciation in refined vegetable oil blends using an innovative combination of chromatographic and spectroscopic techniques

European Regulation 1169/2011 requires producers of foods that contain refined vegetable oils to label the oil types. A novel rapid and staged methodology has been developed for the first time to identify common oil species in oil blends. The qualitative method consists of a combination of a Fourier Transform Infrared (FTIR) spectroscopy to profile the oils and fatty acid chromatographic analysis to confirm the composition of the oils when required. Calibration models and specific classification criteria were developed and all data were fused into a simple decision-making system. The single lab validation of the method demonstrated the very good performance (96% correct classification, 100% specificity, 4% false positive rate). Only a small fraction of the samples needed to be confirmed with the majority of oils identified rapidly using only the spectroscopic procedure. The results demonstrate the huge potential of the methodology for a wide range of oil authenticity work.

COD and NH4-N Estimation in the Inflow of Wastewater Treatment Plants using Machine Learning Techniques

The in-line measurement of COD and NH4-N in the WWTP inflow is crucial for the timely monitoring of biological wastewater treatment processes and for the development of advanced control strategies for optimized WWTP operation. As a direct measurement of COD and NH4-N requires expensive and high maintenance in-line probes or analyzers, an approach estimating COD and NH4-N based on standard and spectroscopic in-line inflow measurement systems using Machine Learning Techniques is presented in this paper. The results show that COD estimation using Radom Forest Regression with a normalized MSE of 0.3, which is sufficiently accurate for practical applications, can be achieved using only standard in-line measurements. In the case of NH4-N, a good estimation using Partial Least Squares Regression with a normalized MSE of 0.16 is only possible based on a combination of standard and spectroscopic in-line measurements. Furthermore, the comparison of regression and classification methods shows that both methods perform equally well in most cases.

Comparison of three molecular techniques for typing Pseudomonas aeruginosa isolates in sputum samples from patients with cystic fibrosis

Monitoring the emergence and transmission of Pseudomonas aeruginosa strains among cystic fibrosis (CF) patients is important for infection control in CF centers internationally. A recently developed multilocus sequence typing (MLST) scheme is used for epidemiologic analyses of P. aeruginosa outbreaks; however, little is known about its suitability for isolates from CF patients compared with that of pulsed-field gel electrophoresis (PFGE) and enterobacterial repetitive intergenic consensus-PCR (ERIC-PCR). As part of a prevalence study of P. aeruginosa strains in Australian CF clinics, we compared the discriminatory power and concordance of ERIC-PCR, PFGE, and MLST among 93 CF sputum and 11 control P. aeruginosa isolates. PFGE and MLST analyses were also performed on 30 paired isolates collected 85 to 354 days apart from 30 patients attending two CF centers separated by 3,600 kilometers in order to detect within-host evolution. Each of the three methods displayed high levels of concordance and discrimination; however, overall lower discrimination was seen with ERIC-PCR than with MLST and PFGE. Analysis of the 50 ERIC-PCR types yielded 54 PFGE types, which were related by ≤ 6 band differences, and 59 sequence types, which were classified into 7 BURST groups and 42 singletons. MLST also proved useful for detecting novel and known strains and for inferring relatedness among unique PFGE types. However, 47% of the paired isolates produced PFGE patterns that within 1 year differed by one to five bands, whereas with MLST all paired isolates remained identical. MLST thus represents a categorical analysis tool with resolving power similar to that of PFGE for typing P. aeruginosa. Its focus on highly conserved housekeeping genes is particularly suited for long-term clinical monitoring and detecting novel strains.