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Cadmium (Cd) uptake and accumulation in crop plants, especially in wheat (Triticum aestivum) and rice (Oryza sativa) is one of the main concerns for food security worldwide. A field experiment was done to investigate the effects of limestone, lignite, and biochar on growth, physiology and Cd uptake in wheat and rice under rotation irrigated with raw effluents. Initially, each treatment was applied...
Sequential dictionary learning via the K-SVD algorithm has been revealed as a successful alternative to conventional data driven methods such as independent component analysis (ICA) for functional magnetic resonance imaging (fMRI) data analysis. fMRI data sets are however structured data matrices with notions of spatio-temporal correlation. This prior information has not been included in the K-SVD...
In this paper, the effect of temporal autocorrelations in functional magnetic resonance imaging (fMRI) data on sparse dictionary learning (SDL) is addressed. For sparse general linear model (sGLM), the fMRI time-series is modeled as a linear mixture of several signals such as neural dynamics, structured noise, random noise and unexplained signal variations on the basis of spatial sparseness. These...
Recovering region-specific hemodynamic response function (HRF) in noisy fMRI data is essential to characterize the temporal dynamics of functionally coherent brain regions during activation. Data-driven techniques not based on sparsity fails to recover sub-region HRFs from overlapping regions of interest (ROIs) in task-related activations. This paper exploits spatial sparsity for recovering distinct...
The paper addresses several constraints associated with wide scale deployment of grid integrated renewable energy system such as stability, active and reactive power regulation, LVRT (Low Voltage Ride Through), power factor improvement and demand side management. While highlighting its challenges, potential solutions to the problems are also discussed using different FACTS (Flexible AC Transmission...
In this paper, we propose an effective technique to analyze task-based functional connectivity across multiple subjects for functional magnetic resonance imaging (fMRI) data. Instead of applying the assumption of group-independence or multiset correlation maximization, an alternative approach is adopted based on a combined framework of sparse dictionary learning (SDL) and multi-set canonical correlation...
This paper addresses the problem of scanner induced low frequency drift estimation in order to improve the significance of functional magnetic resonance imaging (fMRI) data for statistical analysis. A novel technique is presented to estimate the drift parameters using a sparse general linear model (sGLM) framework. The fMRI signal is modeled as a linear mixture of several signals such as low frequency...
Data driven analysis methods such as independent component analysis (ICA) have proven to be well suited for analyzing functional magnetic resonance imaging (fMRI) data. Instead of using the independence assumption as in ICA approaches, we use the sparsity assumption to propose a novel overcom-plete dictionary learning algorithm for statistical analysis of fMRI data. The proposed method differs from...
A principal component analysis (PCA) based dictionary initialization approach accompanied by a computationally efficient dictionary learning algorithm for statistical analysis of functional magnetic resonance imaging (fMRI) is proposed. It replaces a singular value decomposition (SVD) computation with an approximate solution to obtain a local minima for a given initial dictionary. The K-SVD has been...
In this paper a novel framework that combines data-driven methods is proposed for functional connectivity analysis of functional magnetic resonance imaging (fMRI) data. The basic idea is to overcome the shortcomings of compressed sensing based data-driven method by incorporating canonical correlation analysis (CCA) to extract a more meaningful temporal profile that is based solely on underlying brain...
The univariate approach without a smoothing filter for detecting activation patterns in functional magnetic resonance imaging (fMRI) data suffers from a low sensitivity due to presence of high noise. The poor performance of univariate methods such as ordinary correlation is due to lack of their ability to take advantage of spatial correlation that exists in fMR images among group of neighboring voxels...
Lighting constitutes a major portion of electricity consumption in commercial and industrial sector. To some extent, developing countries like Pakistan can meet the energy demand by incorporating energy conservation policy in their Demand Side Management (DSM) program. This paper presents a method to optimize the energy consumption by carrying out lighting audit. The data of Siemens Pakistan department...
This paper investigates the usage of Discrete-time Linear Model Predictive Control in controlling a nonlinear Coupled Tanks System. Two different schemes of Model Predictive control are employed. To begin with, a basic Model Predictive Control based on Generalized Predictive Control is used and then a Model Predictive Control approach based on Laguerre functions. Simulation results have been included...
Brain networks explore the dependence relationships between brain regions under consideration through the estimation of the precision matrix. An approach based on linear regression is adopted here for estimating the partial correlation matrix from functional brain imaging data. Knowing that brain networks are sparse and hierarchical, the l1-norm penalized regression has been used to estimate sparse...
Extracting the directional interaction between activated brain areas from functional magnetic resonance imaging (fMRI) time series measurements of their activity is a significant step in understanding the process of brain functions. In this paper, the directional interaction between fMRI time series characterizing the activity of two neuronal sites is quantified using two measures; one derived based...
Emerging CMOS-based microelectrode array devices allow for recording and stimulation of neuronal networks at high spatiotemporal resolution. The assignment of spiking events to individual neurons, a problem referred to as “spike sorting”, is required for the analysis of neuronal recordings. For closed-loop experiments, where a feedback stimulus is applied in response to neuronal spiking patterns,...
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