Quantitative single point imaging with compressed sensing

A novel approach with respect to single point imaging (SPI), compressed sensing, is presented here that is shown to significantly reduce the loss of accuracy of reconstructed images from under-sampled acquisition data. SPI complements compressed sensing extremely well as it allows unconstrained selection of sampling trajectories. Dynamic processes featuring short NMR signal can thus be more rapidly imaged, in our case the absorption of moisture by a cereal-based wafer material, with minimal loss of image quantification. The absolute moisture content distribution is recovered via a series of images acquired with variable phase encoding times allowing extrapolation to time zero for each image pixel and the effective removal of contrast.     

The impact of sampling time on peak capacity and analysis speed in on-line comprehensive two-dimensional liquid chromatography

Comprehensive two-dimensional liquid chromatography (2DLC) offers a number of practical advantages over optimized one-dimensional LC in peak capacity and thus in resolving power. The traditional “product rule” for overall peak capacity for a 2DLC system significantly overestimates peak capacity because it neglects under-sampling of the first dimension separation. Here we expand on previous work by more closely examining the effects of the first dimension peak capacity and gradient time, and the second dimension cycle times on the overall peak capacity of the 2DLC system. We also examine the effects of re-equilibration time on under-sampling as measured by the under-sampling factor and the influence of molecular type (peptide vs. small molecule) on peak capacity. We show that in fast 2D separations (less than 1 h), the second dimension is more important than the first dimension in determining overall peak capacity and conclude that extreme measures to enhance the first dimension peak capacity are usually unwarranted. We also examine the influence of sample types (small molecules vs. peptides) on second dimension peak capacity and peak capacity production rates, and how the sample type influences optimum second dimension gradient and re-equilibration times.     

Dictionary learning based sinogram inpainting for CT sparse reconstruction

In CT (computed tomography), reconstruction from undersampling projection data is often ill-posed and suffers from severe artifact in the reconstructed images. To overcome this problem, this paper proposes a sinogram inpainting method based on recently rising sparse representation technology. In this approach, a dictionary learning based inpainting is used to estimate the missing projection data. The final image can be reconstructed by the analytic filtered back projection (FBP) reconstruction. We conduct experiments using both simulated and real phantom data. Compared to the comparative interpolation method, visual and numerical results validate the clinical potential of the proposed method.     

LNA linearization solution for direct conversion receiver using under-sampling technique in reference receiver

The paper proposes a method for linearizing low noise amplifiers (LNAs) in multichannel direct conversion receivers. The proposed direct conversion receiver (DCR) uses a linear reference receiver to extract distortion information, which is then fed to an adaptive circuit for linearizing the main channel signal. The proposed DCR differs from prior LNA linearization techniques in that the reference channel in the proposed DCR uses analog to digital converter (ADC) with an undersampling technique to extract reference information. The low-speed ADC also serves as a downconverter, shifting radio frequency (RF) signal to baseband and allowing for all further linearization processing to be performed digitally at a low-sampling data rate. This significantly reduces cost, design complexity, and energy consumption. The effectiveness of the proposed design is theoretically verified through MATLAB simulation and practically measured for a 65 Mhz band of ultra-high frequency (UHF) DCR capable of simultaneously receiving four 16–QAM channels of the same bandwidth of 4 Mhz. The MATLAB software simulation results show that the proposed approach significantly improved the signal-to-noise and distortion ratio (SNDR) for the channel of interest by approximately 30 dB in the worst distorted channel. For hardware implementation, the distorted signals are sampled from a commercial LNA (ZFL–500LN+) by a customized FPGA board. Results from measurements show an improvement of 14.6% for error vector magnitude (EVM) in a strong distortion scenario of 16–QAM modulation signal.     

Peak capacity optimization in comprehensive two dimensional liquid chromatography: a practical approach

In this work we develop a practical approach to optimization in comprehensive two dimensional liquid chromatography (LC x LC) which incorporates the important under-sampling correction and is based on the previously developed gradient implementation of the Poppe approach to optimizing peak capacity. The Poppe method allows the determination of the column length, flow rate as well as initial and final eluent compositions that maximize the peak capacity at a given gradient time. It was assumed that gradient elution is applied in both dimensions and that various practical constraints are imposed on both the initial and final mobile phase composition in the first dimension separation. It was convenient to consider four different classes of solute sets differing in their retention properties. The major finding of this study is that the under-sampling effect is very important and causes some unexpected results including the important counter-intuitive observation that under certain conditions the optimum effective LC x LC peak capacity is obtained when the first dimension is deliberately run under sub-optimal conditions. In addition, we found that the optimum sampling rate in this study is rather slower than reported in previous studies and that it increases with longer first dimension gradient times.     

An efficient algorithm coupled with synthetic minority over-sampling technique to classify imbalanced PubChem BioAssay data

It is common that imbalanced datasets are often generated from high-throughput screening (HTS). For a given dataset without taking into account the imbalanced nature, most classification methods tend to produce high predictive accuracy for the majority class, but significantly poor performance for the minority class. In this work, an efficient algorithm, GLMBoost, coupled with Synthetic Minority Over-sampling TEchnique (SMOTE) is developed and utilized to overcome the problem for several imbalanced datasets from PubChem BioAssay. By applying the proposed combinatorial method, those data of rare samples (active compounds), for which usually poor results are generated, can be detected apparently with high balanced accuracy (Gmean). As a comparison with GLMBoost, Random Forest (RF) combined with SMOTE is also adopted to classify the same datasets. Our results show that the former (GLMBoost + SMOTE) not only exhibits higher performance as measured by the percentage of correct classification for the rare samples (Sensitivity) and Gmean, but also demonstrates greater computational efficiency than the latter (RF + SMOTE). Therefore, we hope that the proposed combinatorial algorithm based on GLMBoost and SMOTE could be extensively used to tackle the imbalanced classification problem.