Small Subregions Research Articles

MPPT Method for PV Systems Under Partially Shaded Conditions by Approximating I–V Curve

Partially shaded (PS) photovoltaic (PV) arrays have multiple peaks at their P–V characteristic. Although conventional maximum power point tracking (MPPT) algorithms are successful when PV arrays are under uniform irradiance conditions (UICs), their tracking speeds are low and may fail to track global maximum power point (GMPP) for PS arrays. Several MPPT algorithms have been proposed for PS arrays. Most of them require numerous samplings which decreases MPPT speed and increases energy loss. The proposed method in this paper gets the GMPP deterministically and very fast. It intelligently takes some samples from the array's P–V curve and divides the search voltage range into small subregions. Then, it approximates the I–V curve of each subregion with a simple curve, and accordingly estimates an upper limit for the array power in that subregion. Next, by comparing the measured real power values with the estimated upper limits, the search region of GMPP is limited, and based on some defined criteria, the vicinity of GMPP is determined. Simulation and experimental results and comparisons are presented to highlight the performance and superiority of the proposed approach.

Learning Filter Bank Sparsifying Transforms

Data is said to follow the transform (or analysis) sparsity model if it becomes sparse when acted on by a linear operator called a sparsifying transform. Several algorithms have been designed to learn such a transform directly from data, and data-adaptive sparsifying transforms have demonstrated excellent performance in signal restoration tasks. Sparsifying transforms are typically learned using small sub-regions of data called patches, but these algorithms often ignore redundant information shared between neighboring patches. We show that many existing transform and analysis sparse representations can be viewed as filter banks, thus linking the local properties of patch-based model to the global properties of a convolutional model. We propose a new transform learning framework where the sparsifying transform is an undecimated perfect reconstruction filter bank. Unlike previous transform learning algorithms, the filter length can be chosen independently of the number of filter bank channels. Numerical results indicate filter bank sparsifying transforms outperform existing patch-based transform learning for image denoising while benefiting from additional flexibility in the design process.

Photometric metallicity map of the Small Magellanic Cloud

We have created an estimated metallicity map of the Small Magellanic Cloud (SMC) using the Magellanic Cloud Photometric Survey (MCPS) and Optical Gravitational Lensing Experiment (OGLE III) photometric data. This is a first of its kind map of metallicity up to a radius of $\sim$ 2.5$^{\circ}$. We identify the RGB in the V, (V$-$I) colour magnitude diagrams of small subregions of varying sizes in both data sets. We use the slope of the RGB as an indicator of the average metallicity of a subregion, and calibrate the RGB slope to metallicity using available spectroscopic data for selected subregions. The average metallicity of the SMC is found to be [Fe/H] = $-$0.94 dex ($\sigma$[Fe/H] = 0.09) from OGLE III, and [Fe/H] = $-$0.95 dex ($\sigma$[Fe/H] = 0.08) from MCPS. We confirm a shallow but significant metallicity gradient within the inner SMC up to a radius of 2.5$^{\circ}$ ($-$0.045$\pm$0.004 dex deg$^{-1}$ to $-$0.067$\pm$0.006 dex deg$^{-1}$).

Locomotor speed control circuits in the caudal brainstem.

Locomotion is a universal behaviour that provides animals with the ability to move between places. Classical experiments have used electrical microstimulation to identify brain regions that promote locomotion, but the identity of neurons that act as key intermediaries between higher motor planning centres and executive circuits in the spinal cord has remained controversial. Here we show that the mouse caudal brainstem encompasses functionally heterogeneous neuronal subpopulations that have differential effects on locomotion. These subpopulations are distinguishable by location, neurotransmitter identity and connectivity. Notably, glutamatergic neurons within the lateral paragigantocellular nucleus (LPGi), a small subregion in the caudal brainstem, are essential to support high-speed locomotion, and can positively tune locomotor speed through inputs from glutamatergic neurons of the upstream midbrain locomotor region. By contrast, glycinergic inhibitory neurons can induce different forms of behavioural arrest mapping onto distinct caudal brainstem regions. Anatomically, descending pathways of glutamatergic and glycinergic LPGi subpopulations communicate with distinct effector circuits in the spinal cord. Our results reveal that behaviourally opposing locomotor functions in the caudal brainstem were historically masked by the unexposed diversity of intermingled neuronal subpopulations. We demonstrate how specific brainstem neuron populations represent essential substrates to implement key parameters in the execution of motor programs.

Assessment of the impact of modeling axial compression on PET image reconstruction.

To comprehensively evaluate both the acceleration and image-quality impacts of axial compression and its degree of modeling in fully 3D PET image reconstruction. Despite being used since the very dawn of 3D PET reconstruction, there are still no extensive studies on the impact of axial compression and its degree of modeling during reconstruction on the end-point reconstructed image quality. In this work, an evaluation of the impact of axial compression on the image quality is performed by extensively simulating data with span values from 1 to 121. In addition, two methods for modeling the axial compression in the reconstruction were evaluated. The first method models the axial compression in the system matrix, while the second method uses an unmatched projector/backprojector, where the axial compression is modeled only in the forward projector. The different system matrices were analyzed by computing their singular values and the point response functions for small subregions of the FOV. The two methods were evaluated with simulated and real data for the Biograph mMR scanner. For the simulated data, the axial compression with span values lower than 7 did not show a decrease in the contrast of the reconstructed images. For span 11, the standard sinogram size of the mMR scanner, losses of contrast in the range of 5-10 percentage points were observed when measured for a hot lesion. For higher span values, the spatial resolution was degraded considerably. However, impressively, for all span values of 21 and lower, modeling the axial compression in the system matrix compensated for the spatial resolution degradation and obtained similar contrast values as the span 1 reconstructions. Such approaches have the same processing times as span 1 reconstructions, but they permit significant reduction in storage requirements for the fully 3D sinograms. For higher span values, the system has a large condition number and it is therefore difficult to recover accurately the higher frequencies. Modeling the axial compression also achieved a lower coefficient of variation but with an increase of intervoxel correlations. The unmatched projector/backprojector achieved similar contrast values to the matched version at considerably lower reconstruction times, but at the cost of noisier images. For a line source scan, the reconstructions with modeling of the axial compression achieved similar resolution to the span 1 reconstructions. Axial compression applied to PET sinograms was found to have a negligible impact for span values lower than 7. For span values up to 21, the spatial resolution degradation due to the axial compression can be almost completely compensated for by modeling this effect in the system matrix at the expense of considerably larger processing times and higher intervoxel correlations, while retaining the storage benefit of compressed data. For even higher span values, the resolution loss cannot be completely compensated possibly due to an effective null space in the system. The use of an unmatched projector/backprojector proved to be a practical solution to compensate for the spatial resolution degradation at a reasonable computational cost but can lead to noisier images.

RNA-Seq investigations of human post-mortem trigeminal ganglia.

Background The trigeminal ganglion contains neurons that relay sensations of pain, touch, pressure, and many other somatosensory modalities to the central nervous system. The ganglion is also a reservoir for latent herpes virus 1 infection. To gain a better understanding of molecular factors contributing to migraine and headache, transcriptome analyses were performed on postmortem human trigeminal ganglia. Methods RNA-Seq measurements of gene expression were conducted on small sub-regions of 16 human trigeminal ganglia. The samples were also characterized for transcripts derived from viral and microbial genomes. Herpes simplex virus 1 (HSV-1) antibodies in blood were measured using the luciferase immunoprecipitation assay. Results Observed molecular heterogeneity could be explained by sampling of anatomically distinct sub-regions of the excised ganglia consistent with neurally-enriched and non-neural, i.e. Schwann cell, enriched subregions. The levels of HSV-1 transcripts detected in trigeminal ganglia correlated with blood levels of HSV-1 antibodies. Multiple migraine susceptibility genes were strongly expressed in neurally-enriched trigeminal samples, while others were expressed in blood vessels. Conclusions These data provide a comprehensive human trigeminal transcriptome and a framework for evaluation of inhomogeneous post-mortem tissues through extensive quality control and refined downstream analyses for RNA-Seq methodologies. Expression profiling of migraine susceptibility genes identified by genetic association appears to emphasize the blood vessel component of the trigeminovascular system. Other genes displayed enriched expression in the trigeminal compared to dorsal root ganglion, and in-depth transcriptomic analysis of the KCNK18 gene underlying familial migraine shows selective neural expression within two specific populations of ganglionic neurons. These data suggest that expression profiling of migraine-associated genes can extend and amplify the underlying neurobiological insights obtained from genetic association studies.

Heterogeneous extracellular dopamine regulation in the subregions of the olfactory tubercle.

Recent studies show that dense dopamine (DA) innervation from the ventral tegmental area to the olfactory tubercle (OT) may play an important role in processing multisensory information pertaining to arousal and reward, yet little is known about DA regulation in the OT. This is mainly due to the anatomical limitations of conventional methods of determining DA dynamics in small heterogeneous OT subregions located in the ventral most part of the brain. Additionally, there is increasing awareness that anteromedial and anterolateral subregions of the OT have distinct functional roles in natural and psychostimulant drug reinforcement as well as in regulating other types of behavioral responses, such as aversion. Here, we compared extracellular DA regulation (release and clearance) in three subregions (anteromedial, anterolateral, and posterior) of the OT of urethane-anesthetized rats, using invivo fast-scan cyclic voltammetry following electrical stimulation of ventral tegmental area dopaminergic cell bodies. The neurochemical, anatomical, and pharmacological evidence confirmed that the major electrically evoked catecholamine in the OT was DA across both its anteroposterior and mediolateral extent. While both D2 autoreceptors and DA transporters play important roles in regulating DA evoked in OT subregions, DA in the anterolateral OT was regulated less by the D2 receptors when compared to other OT subregions. Comparing previous data from other DA rich ventral striatum regions, the slow DA clearance across the OT subregions may lead to a high extracellular DA concentration and contribute towards volume transmission. These differences in DA regulation in the terminals of OT subregions and other limbic structures will help us understand the neural regulatory mechanisms of DA in the OT, which may elucidate its distinct functional contribution in the ventral striatum towards mediating aversion, reward and addiction processes.

A Quadrigeminal Scheme based on Event Reporting Scalar Premier Selection for Camera Actuation in Wireless Multimedia Sensor Networks

In this article, we have proposed a novel quadrigeminal scheme for selecting event-reporting scalar premiers that inform the prevailing event information to their corresponding camera sensors while contributing maximal event area coverage along with minimal redundant data transmission. In this work, we have divided the monitored region into a number of sub-regions. Subsequently, each sub-region is further divided into smaller sub-regions. In each of the larger sub-regions, four scalar premiers are chosen in such a way that a single scalar premier exists in one smaller sub-region. Subsequently, the cameras are activated by matching their scalar premier ids with each other based on descending order of their total scalar premier count values. The efficiency of the proposed scheme has been verified by experimental investigation and comparative assessment against three well known existing algorithms. The least camera actuation, improved coverage ratio along with reduced values of event-loss ratio, redundancy ratio and energy expenditure obtained from the experimentation affirm the effectiveness of the proposed method.

Inferring Master Painters' Esthetic Biases from the Statistics of Portraits.

The Processing Fluency Theory posits that the ease of sensory information processing in the brain facilitates esthetic pleasure. Accordingly, the theory would predict that master painters should display biases toward visual properties such as symmetry, balance, and moderate complexity. Have these biases been occurring and if so, have painters been optimizing these properties (fluency variables)? Here, we address these questions with statistics of portrait paintings from the Early Renaissance period. To do this, we first developed different computational measures for each of the aforementioned fluency variables. Then, we measured their statistics in 153 portraits from 26 master painters, in 27 photographs of people in three controlled poses, and in 38 quickly snapped photographs of individual persons. A statistical comparison between Early Renaissance portraits and quickly snapped photographs revealed that painters showed a bias toward balance, symmetry, and moderate complexity. However, a comparison between portraits and controlled-pose photographs showed that painters did not optimize each of these properties. Instead, different painters presented biases toward different, narrow ranges of fluency variables. Further analysis suggested that the painters' individuality stemmed in part from having to resolve the tension between complexity vs. symmetry and balance. We additionally found that constraints on the use of different painting materials by distinct painters modulated these fluency variables systematically. In conclusion, the Processing Fluency Theory of Esthetic Pleasure would need expansion if we were to apply it to the history of visual art since it cannot explain the lack of optimization of each fluency variables. To expand the theory, we propose the existence of a Neuroesthetic Space, which encompasses the possible values that each of the fluency variables can reach in any given art period. We discuss the neural mechanisms of this Space and propose that it has a distributed representation in the human brain. We further propose that different artists reside in different, small sub-regions of the Space. This Neuroesthetic-Space hypothesis raises the question of how painters and their paintings evolve across art periods.

Rapid 4D FWI using a local wave solver

Much of the computational cost involved in full-waveform inversion comes from the solution of the wave equation in a large domain. These computations must be done for the entire domain through which we expect waves to pass for a particular survey, despite the fact that our region of interest is often significantly smaller. In addition to the wasted time spent propagating waves through less important parts of the model, computing updates on the entire domain may result in slower convergence of the inversion algorithm due to the larger model space. This can be especially important in 4D seismic monitoring, where we often see the majority of changes within a small subregion of the total domain, such as the reservoir. We present a local wave solver that accurately computes the solution of the wave equation within only a subdomain of the region covered by the survey, representing a significant cost saving in the computation of full-waveform inversion. We also show how this solver can improve the resulting velocity estimates in full-waveform inversion for time-lapse applications and observe that the local solver requires fewer iterations to converge than does the full-domain solver.

The structural connectivity of higher order association cortices reflects human functional brain networks

Human higher cognition arises from the main tertiary association cortices including the frontal, temporal and parietal lobes. Many studies have suggested that cortical functions must be shaped or emerge from the pattern of underlying physical (white matter) connectivity. Despite the importance of this hypothesis, there has not been a large-scale analysis of the white-matter connectivity within and between these associative cortices. Thus, we explored the pattern of intra- and inter-lobe white matter connectivity between multiple areas defined in each lobe. We defined 43 regions of interest on the lateral associative cortex cytoarchitectonically (6 regions of interest – ROIs in the frontal lobe and 17 ROIs in the parietal lobe) and anatomically (20 ROIs in the temporal lobe) on individuals' native space. The results demonstrated that intra-region connectivity for all 3 lobes was dense and graded generally. In contrary, the inter-lobe connectivity was relatively discrete and regionally specific such that only small sub-regions exhibited long-range connections to another lobe. The long-range connectivity was mediated by 6 major associative white matter tracts, consistent with the notion that these higher cognitive functions arises from brain-wide distributed connectivity. Using graph-theory network analysis we revealed five physically-connected sub-networks, which correspond directly to five known functional networks. This study provides strong and direct evidence that core functional brain networks mirror the brain's structural connectivity.

Geometry-aware partitioning of complex domains for parallel quad meshing

We develop a partitioning algorithm to decompose complex 2D data into small and simple subregions suitable for effective distributed and parallel quadrilateral mesh generation. To support high-quality quad mesh generation, the partitioning reduces to solving an integer quadratic optimization problem with linear constraints. Directly solving this problem is expensive for large-scale data. Hence, we also suggest a more efficient two-step algorithm to obtain an approximate solution. First, we partition the region into a set of cells using L∞ Centroidal Voronoi Tessellation (CVT), then we solve a graph partitioning on the dual graph of this CVT to minimize the total partitioning boundary length, while enforcing the load balancing and each subregion’s connectivity. With this decomposition, subregions are distributed to multiple processors for parallel mesh generation. Through comparisons on the quality of the final meshes and the performance of simulations run on these meshes, we show that our decomposition algorithm outperforms existing partitioning approaches by offering more simulation-friendly regular meshes.

Altered responsiveness of BNST and amygdala neurons in trauma-induced anxiety.

A highly conserved network of brain structures regulates the expression of fear and anxiety in mammals. Many of these structures display abnormal activity levels in post-traumatic stress disorder (PTSD). However, some of them, like the bed nucleus of the stria terminalis (BNST) and amygdala, are comprised of several small sub-regions or nuclei that cannot be resolved with human neuroimaging techniques. Therefore, we used a well-characterized rat model of PTSD to compare neuronal properties in resilient vs PTSD-like rats using patch recordings obtained from different BNST and amygdala regions in vitro. In this model, a persistent state of extreme anxiety is induced in a subset of susceptible rats following predatory threat. Previous animal studies have revealed that the central amygdala (CeA) and BNST are differentially involved in the genesis of fear and anxiety-like states, respectively. Consistent with these earlier findings, we found that between resilient and PTSD-like rats were marked differences in the synaptic responsiveness of neurons in different sectors of BNST and CeA, but whose polarity was region specific. In light of prior data about the role of these regions, our results suggest that control of fear/anxiety expression is altered in PTSD-like rats such that the influence of CeA is minimized whereas that of BNST is enhanced. A model of the amygdalo-BNST interactions supporting the PTSD-like state is proposed.

Reproducibility of Neurochemical Profile Quantification in Pregenual Cingulate, Anterior Midcingulate, and Bilateral Posterior Insular Subdivisions Measured at 3 Tesla.

The current report assessed measurement reproducibility of proton magnetic resonance spectroscopy at 3 Tesla in the left and right posterior insular, pregenual anterior cingulate, and anterior midcingulate cortices. Ten healthy male volunteers aged 21–30 years were tested at four different days, of which nine were included in the data analysis. Intra- and inter-subject variability of myo-inositol, creatine, glutamate, total-choline, total-N-acetylaspartate, and combined glutamine–glutamate were calculated considering the influence of movement parameters, age, daytime of measurements, and tissue composition. Overall mean intra-/inter-subject variability for all neurochemicals combined revealed small mean coefficients of variation across the four regions: 5.3/9.05% in anterior midcingulate, 6.6/8.84% in pregenual anterior cingulate, 7.3/10.00% in left posterior and 8.2/10.55% in right posterior insula. Head movement, tissue composition and day time revealed no significant explanatory variance contribution suggesting a negligible influence on the data. A strong correlation between Cramer–Rao Lower Bounds (a measure of fitting errors) and the mean intra-subject coefficients of variation (r = 0.799, p < 0.001) outlined the importance of low fitting errors in order to obtain robust and finally meaningful measurements. The present findings confirm proton magnetic resonance spectroscopy as a reliable tool to measure brain neurochemistry in small subregions of the human brain.

Mixed scale joint graphical lasso.

We have developed a method for estimating brain networks from fMRI datasets that have not all been measured using the same set of brain regions. Some of the coarse scale regions have been split in smaller subregions. The proposed penalized estimation procedure selects undirected graphical models with similar structures that combine information from several subjects and several coarseness scales. Both within-scale edges and between-scale edges that identify possible connections between a large region and its subregions are estimated.

Large-Scale Meta-Analysis of Human Medial Frontal Cortex Reveals Tripartite Functional Organization.

The functional organization of human medial frontal cortex (MFC) is a subject of intense study. Using fMRI, the MFC has been associated with diverse psychological processes, including motor function, cognitive control, affect, and social cognition. However, there have been few large-scale efforts to comprehensively map specific psychological functions to subregions of medial frontal anatomy. Here we applied a meta-analytic data-driven approach to nearly 10,000 fMRI studies to identify putatively separable regions of MFC and determine which psychological states preferentially recruit their activation. We identified regions at several spatial scales on the basis of meta-analytic coactivation, revealing three broad functional zones along a rostrocaudal axis composed of 2-4 smaller subregions each. Multivariate classification analyses aimed at identifying the psychological functions most strongly predictive of activity in each region revealed a tripartite division within MFC, with each zone displaying a relatively distinct functional signature. The posterior zone was associated preferentially with motor function, the middle zone with cognitive control, pain, and affect, and the anterior with reward, social processing, and episodic memory. Within each zone, the more fine-grained subregions showed distinct, but subtler, variations in psychological function. These results provide hypotheses about the functional organization of medial prefrontal cortex that can be tested explicitly in future studies. Activation of medial frontal cortex in fMRI studies is associated with a wide range of psychological states ranging from cognitive control to pain. However, this high rate of activation makes it challenging to determine how these various processes are topologically organized across medial frontal anatomy. We conducted a meta-analysis across nearly 10,000 studies to comprehensively map psychological states to discrete subregions in medial frontal cortex using relatively unbiased data-driven methods. This approach revealed three distinct zones that differed substantially in function, each of which were further subdivided into 2-4 smaller subregions that showed additional functional variation. Each individual region was recruited by multiple psychological states, suggesting subregions of medial frontal cortex are functionally heterogeneous.

Scattering of light from a stationary nonuniformly correlated medium.

A mathematical model for describing a three-dimensional, nonuniformly correlated, weak scattering medium is introduced. It is assumed that the medium occupies a volume in which a relatively high degree of spatial refractive index correlation may exist in some small sub-region, whether on or off its center. Within the validity of the first-order Born approximation, the far-zone spectral density of a plane wave scattered from such a medium is determined. We find that the introduced scatterer has a remarkable capability for producing a focused peak on or off the scattering axis. It is shown that by altering the position of the high correlation center within the medium, it is possible to control the peak position and peak intensity of the scattered spectral density. For certain combinations of the average medium's correlation width and the location of the highly correlated region, the scattered spectral density (intensity) may also acquire one-dimensional sidelobes.

An algorithm for approximating the Pareto set of the multiobjective set covering problem

The multiobjective set covering problem (MOSCP), a challenging combinatorial optimization problem, has received limited attention in the literature. This paper presents a heuristic algorithm to approximate the Pareto set of the MOSCP. The proposed algorithm applies a local branching approach on a tree structure and is enhanced with a node exploration strategy specially developed for the MOSCP. The main idea is to partition the search region into smaller subregions based on the neighbors of a reference solution and then to explore each subregion for the Pareto points of the MOSCP. Numerical experiments for instances with two, three and four objectives set covering problems are reported. Results on a performance comparison with benchmark algorithms from the literature are also included and show that the new algorithm is competitive and performs best on some instances.

Time-Domain Iterative Multiregion Technique for 3-D Scattering and Radiation Problems

Integration of the finite-difference time-domain (FDTD) method into the iterative multiregion (IMR) technique, an iterative approach used to solve large-scale electromagnetic scattering and radiation problems, is presented in this paper. The idea of the IMR technique is to divide a large problem domain into smaller subregions, solve each subregion separately, and combine the solutions of subregions after introducing the effect of interaction to obtain solutions at multiple frequencies for the large domain. Solutions of the subregions using frequency domain solvers has been the preferred approach as such; solutions using time domain solvers require computationally expensive bookkeeping of time signals. In this paper, we present an algorithm that makes it feasible to use the FDTD method, a time domain numerical technique, in the IMR technique to obtain solutions at a prespecified number of frequencies in a single simulation. A hybrid method integrated into the IMR technique is also presented in this paper. This hybrid method combines the desirable features of the FDTD method and the method of moments (MoM) to solve radiation problems more efficiently. As a result, a considerable reduction in memory storage requirements and computation time is achieved.

Temperature-dependent remote control of polarization and coherence intensity with sender’s pure initial state

We study the remote creation of the polarization and intensity of the first-order coherence (or coherence intensity) in long spin-1/2 chains with one qubit sender and receiver. Therewith we use a physically motivated initial condition with the pure state of the sender and the thermodynamical equilibrium state of the other nodes. The main part of the creatable region is a one-to-one map of the initial-state (control) parameters, except the small subregion twice covered by the control parameters, which appears owing to the chosen initial state. The polarization and coherence intensity behave differently in the state creation process. In particular, the coherence intensity can not reach any significant value unless the polarization is large in long chains (unlike the short ones), but the opposite is not true. The coherence intensity vanishes with an increase in the chain length, while the polarization (by absolute value) is not sensitive to this parameter. We represent several characteristics of the creatable polarization and coherence intensity and describe their relation to the parameters of the initial state. The link to the eigenvalue-eigenvector parametrization of the receiver's state-space is given.