Spectral Partitioning Research Articles

Substantial increase in detection efficiency for filter array-based spectral sensors.

A method that significantly increases the detection efficiency of filter array-based spectral sensors is proposed. The basic concept involves a wavelength-dependent redistribution of incident light before it reaches the filter elements located in front of the detector. Due to this redistribution, each filter element of the array receives a spatially concentrated amount of a pre-selected and adjusted spectral partition of the entire incident light. This approach can be employed to significantly reduce the reflection and absorption losses of each filter element. The proof-of-concept is demonstrated by a setup that combines a series of consecutively arranged dichroic filters with Fabry-Perot filter arrays. Experimentally, an efficiency increase by a factor larger than 4 compared to a reference system is demonstrated. The optical system is a non-imaging spectrometer, which combines the efficiency enhancement module with the filter arrays, is compact (17.5mm×17.5mm×7.8mm), and integrated completely inside the CCD camera mount.

A hybrid algorithm for task scheduling on heterogeneous multiprocessor embedded systems

Most of the scheduling algorithms proposed for real-time embedded systems, with energy constraints, try to reduce power consumption. However, reducing the power consumption may decrease the computation speed and impact the makespan. Therefore, for real-time embedded systems, makespan and power consumption need to be considered simultaneously. Since task scheduling is an NP-hard problem, most of the proposed scheduling algorithms are not able to find the multi-objective optimal solution. In this paper, we propose a two-phase hybrid task scheduling algorithm based on decomposition of the input task graph, by applying spectral partitioning. The proposed algorithm, called G-SP, assigns each part of the task graph to a low power processor in order to minimize power consumption. Through experiments, we compare the makespan and power consumption of the G-SP against well-known algorithms of this area for a large set of randomly generated and real-world task graphs with different characteristics. The obtained results show that the G-SP outperforms other algorithms in both metrics, under various conditions, involving different numbers of processors and considering several system configurations.

A spectra partition algorithm based on spectral clustering for interval variable selection

Variable selection is recognized as a way to build a robust model and improve its prediction performance in NIR spectral analysis. Interval variable selection as one of two main strategies of variable selection, is believed to have a good spectra interpretation. Many interval variable selection algorithms have been proposed and developed. Among them dividing the spectra into intervals manually is normally needed, and generally the equal-width partition is adopted, which is arbitrary and subjective. Clustering is known for self-discovering the inner structure of data. In this work, a new method called spectral clustering-based interval partition (SCIP), is purposed as an alternative to the equal-width partition. It partitions the spectra by using spectral clustering based on the correlation between variables. When SCIP is adopted, compared with the equal-width partition under the same number of partitions, the commonly used interval variable selection methods, such as forward interval partial least regression (fiPLS), backward interval partial least regression (biPLS), synergy interval partial least regression (siPLS), genetic algorithm-interval partial least regression (GA-iPLS), and interval combination optimization (ICO) can supply more reasonable wavelength intervals and contribute better prediction performances in four near infrared datasets.

Wheel graph strategy for PEV localization of networks

Investigation of eigenvector localization properties of complex networks is not only important for gaining insight into fundamental network problems such as network centrality measure, spectral partitioning, development of approximation algorithms, but is also crucial for understanding many real-world phenomena such as disease spreading, criticality in brain network dynamics. For a network, an eigenvector is said to be localized when most of its components take value near to zero, with a few components taking very high values. In this article, we devise a methodology to construct a principal eigenvector (PEV) localized network from a given input network. The methodology relies on adding a small component having a wheel graph to the given input network. By extensive numerical simulation and an analytical formulation based on the largest eigenvalue of the input network, we compute the size of the wheel graph required to localize the PEV of the combined network. Using the susceptible-infected-susceptible model, we demonstrate the success of this method for various models and real-world networks considered as input networks. We show that on such PEV localized networks, the disease gets localized within a small region of the network structure before the outbreaks. The study is relevant in controlling spreading processes on complex systems represented by networks.

Vertex nomination: The canonical sampling and the extended spectral nomination schemes

Suppose that one particular block in a stochastic block model is of interest, but block labels are only observed for a few of the vertices in the network. Utilizing a graph realized from the model and the observed block labels, the vertex nomination task is to order the vertices with unobserved block labels into a ranked nomination list with the goal of having an abundance of interesting vertices near the top of the list. There are vertex nomination schemes in the literature, including the optimally precise canonical nomination scheme LC and the consistent spectral partitioning nomination scheme LP. While the canonical nomination scheme LC is provably optimally precise, it is computationally intractable, being impractical to implement even on modestly sized graphs.With this in mind, an approximation of the canonical scheme – denoted the canonical sampling nomination schemeLCS – is introduced; LCS relies on a scalable, Markov chain Monte Carlo-based approximation of LC, and converges to LC as the amount of sampling goes to infinity. The spectral partitioning nomination scheme is also extended to the extended spectral partitioning nomination scheme, LEP, which introduces a novel semisupervised clustering framework to improve upon the precision of LP. Real-data and simulation experiments are employed to illustrate the precision of these vertex nomination schemes, as well as their empirical computational complexity.

Decentralized Voltage Control of PowerSystems Using Multi-agent Systems

A comprehensive scheme based on decentralized control, partitioning, multi-agent systems, and fuzzy logic is presented in this paper for the voltage control of power systems. In our proposed smart self-healing method, two types of control agents are defined, namely master and local, which are applied in two steps. In the first step, the power system returns to the normal state after fault occurrence. Immediately after a fault detection in a power system, the system is divided into three subsystems using spectral graph partitioning. Partitioning is conducted based on reactive power flow in transmission lines. For each subsystem, a local control agent and a performance index (PI) are defined. Whenever the PI of a subsystem exceeds its threshold limit, the local control agent uses the Sugeno fuzzy system to intelligently select and apply control actions. In the second step as performed by the master control agent, the power system is transformed to an optimal state by solving the optimization problem. Simulations on a 39-bus New England reveal the effective performance of the proposed method.

Intelligent Vulnerability Analysis for Connectivity and Critical-Area Integrity in IoV

The large-scale connectivity of Internet of Vehicles (IoV) is an important challenge for the Intelligent Transportation Systems (ITS). Intelligence vulnerability analysis is an excellent solution. However, existing methods for analyzing connectivity vulnerability have ignored the existence of critical areas in the system. Due to the heterogeneities of the IoV environments and services, the failure of some specific areas may seriously damage connectivity and system performance. To this end, in this paper we focus on both the dynamic connectivity and the critical-area integrity, and propose an intelligent vulnerability analysis method to effectively identify the critical area of extreme vulnerability. Specifically, we consider an intelligent analysis scenario in which roadside servers continuously learn IoV heterogeneous environment and dynamic topology, and then translate the learning results into a flexible disruption cost problem. Based on this, we utilize the spectral partitioning method to identify the minimum-cost set of topological elements whose failure not only severely damages system connectivity but also disrupts its critical areas. Furthermore, we confirm that the identified set can be used to optimize disruption cost problem, thus intelligently improving vulnerability. Simulation results show that our proposed method can effectively identify vulnerable elements and prevent significant loss in the IoV system connectivity and performance.

Bipartite Graph Partition Based Coclustering With Joint Sparsity for Hyperspectral Images

Clustering for hyperspectral images (HSIs) is a very challenging task because HSIs usually have large spectral variability, high dimensionality, and complex structures. Recently, the sparse representation and dictionary-learning-based spectral clustering (SC) approaches have achieved great success in clustering HSIs. However, SC deals with hyperspectral clustering by one-way clustering, i.e., either dictionary atoms or sparse-representation-based clustering, and only clusters the columns information of adjacency matrix. To overcome these problems, we propose a novel coclustering based on bipartite graph partition (BGP) with joint sparsity for two disjoint sets: dictionary atoms and hyperspectral pixels. First, the structured dictionary constraint is incorporated into a unified joint sparsity constrained optimizing model to learn a structured sparse representation by incorporating spectral and contextual characteristics of HSIs where the joint sparsity coefficients provide related information between hyperspectral pixels and dictionary atoms. Then, a BGP-based classifier is proposed to simultaneously group hyperspectral pixels with the learned dictionary atoms by mapping the joint sparsity coefficients to a non-negative affinity matrix of graph, and simultaneously clusters rows and columns information of adjacency matrix. To solve the BGP problem, we use spectral graph partition algorithm, which uses the left and right singular vectors of an appropriately dictionary-signal adjacent matrix to yield good bipartitionings and the singular vectors solve a real relaxation of the NP-complete BGP problem. The proposed algorithm was tested on three widely used hyperspectral datasets, and experimental results demonstrate that the proposed algorithm outperforms other state-of-the-art methods.

Climatology of Wind-Seas and Swells in the China Seas from Wave Hindcast

The wind-sea and swell climates in the China Seas are investigated by using the 27-yr Integrated Ocean Waves for Geophysical and other Applications (IOWAGA) hindcast data. A comparison is made between the significant wave height from the IOWAGA hindcasts and that from a jointly calibrated altimetry dataset, showing the good performance of the IOWAGA hindcasts in the China Seas. A simple but practical method of diagnosing whether the sea state is wind-sea-dominant or swell-dominant is proposed based on spectral partitioning. Different from the characteristics of wind-seas and swells in the open ocean, the wave fields in the enclosed seas such as the China Seas are predominated by wind-sea events in respect of both frequencies of occurrences and energy weights, due to the island sheltering and limited fetches. The energy weights of wind-seas in a given location is usually more significant than the occurrence probability of wind-sea-dominated events, as the wave energy is higher in the wind-sea events than in the swell events on average and extreme wave heights are mostly related to wind-seas. The most energetic swells in the China Seas (and other enclosed seas) are ‘local swells’, having just propagated out of their generation areas. However, the swells coming from the West Pacific also play an important role in the wave climate of the China Seas, which can only be revealed by partitioning different swell systems in the wave spectra as the energy of them is significantly less than the ‘local swells’.

Structured Optimal Graph-Based Clustering With Flexible Embedding.

In the real world, the duality of high-dimensional data is widespread. The coclustering method has been widely used because they can exploit the co-occurring structure between samples and features. In fact, most of the existing coclustering methods cluster the graphs in the original data matrix. However, these methods fail to output an affinity graph with an explicit cluster structure and still call for the postprocessing step to obtain the final clustering results. In addition, these methods are difficult to find a good projection direction to complete the clustering task on high-dimensional data. In this article, we modify the flexible manifold embedding theory and embed it into the bipartite spectral graph partition. Then, we propose a new method called structured optimal graph-based clustering with flexible embedding (SOGFE). The SOGFE method can learn an affinity graph with an optimal and explicit clustering structure and does not require any postprocessing step. Additionally, the SOGFE method can learn a suitable projection direction to map high-dimensional data to a low-dimensional subspace. We perform extensive experiments on two synthetic data sets and seven benchmark data sets. The experimental results verify the superiority, robustness, and good projection direction selection ability of our proposed method.

Spectral partitioning identifies individual heterogeneity in the functional network topography of ventral and anterior medial prefrontal cortex

Regions of human medial prefrontal cortex (mPFC) and posterior cingulate cortex (PCC) are part of the default network (DN), and additionally are implicated in diverse cognitive functions ranging from autobiographical memory to subjective valuation. Our ability to interpret the apparent co-localization of task-related effects with DN-regions is constrained by a limited understanding of the individual-level heterogeneity in mPFC/PCC functional organization. Here we used cortical surface-based meta-analysis to identify a parcel in human PCC that was more strongly associated with the DN than with valuation effects. We then used resting-state fMRI data and a data-driven network analysis algorithm, spectral partitioning, to partition mPFC and PCC into “DN” and “non-DN” subdivisions in individual participants (n = 100 from the Human Connectome Project). The spectral partitioning algorithm identified individual-level cortical subdivisions that varied markedly across individuals, especially in mPFC, and were reliable across test/retest datasets. Our results point toward new strategies for assessing whether distinct cognitive functions engage common or distinct mPFC subregions at the individual level.

Accurate 3-D Localization of Selected Smart Objects in Optical Internet of Underwater Things

Localization is a fundamental task for the optical Internet of Underwater Things (O-IoUT) to enable various applications, such as data tagging, routing, navigation, and maintaining link connectivity. The accuracy of the localization techniques for O-IoUT greatly relies on the location of the anchors. Therefore, recently, the localization techniques for O-IoUT which optimize the anchor's location have been proposed. However, the optimization of the anchors' location for all the smart objects in the network is not a useful solution. Indeed, in a network of densely populated smart objects, the data collected by some sensors are more valuable than the data collected from other sensors. Therefore, in this article, we propose a 3-D accurate localization technique by optimizing the anchor's location for a set of smart objects. Spectral graph partitioning is used to select the set of valuable sensors. The numerical results show that the proposed technique of optimizing anchor's location for a set of selected sensors provides a better location accuracy.

Community Detection Algorithms for Big Data using Graph Theory

Community detection is a nowadays research problem in the Big Data era related to huge volume, variety, and velocity of data. Big data defines data where normal processing, storage, retrieval fails and require some advanced tools to solve these types of problem. An important tool in the analysis of complex network is community detection. Community detection or community mining is a technique which is used to find the same type of relations in a particular group. Community detection is also known as Graph Clustering. This paper represents Big data in the form of graphs and detects community via some graph algorithms like METIS, Spectral Partitioning, hierarchical clustering, Markov Clustering, Genetic Algorithm based community detection algorithm, etc. Community detection is widely used in various types of disease detection, drug formation, species clustering. It can be also used in social networking sites to control crimes by detecting community bad peoples.

An Improved spectral graph partition intelligent clustering algorithm for low-power wireless networks

Aiming at the problem of energy imbalance and low-energy efficiency in wireless sensor network, a novel energy-balanced clustering routing algorithm based on spectral graph partition is proposed in this paper, which firstly calculates the optimum single-hop distance, balances the energy consumption of cluster heads, and avoids that the number of cluster heads is not fixed and cluster heads are clustered by using the traditional probability function. In addition, the transmission cost function is designed to construct the optimum transmission path to reduce energy consumption. The experimental results show that the network performance of our proposed algorithm is improved, which achieves on low-energy network for loss reduction and load balancing.

On the statistical analysis of ocean wave directional spectra

Given the growing availability of directional spectra of ocean waves, we explore two different statistical approaches to mine large spectra databases: Spectral Partitions Statistics (SPS) and Self-Organizing Maps (SOM). The first method is not new in the literature, while the second one is for the first time here applied to directional wave spectra. The main goal is to improve the characterization of the directional wave climate at a site, providing a more complete and consistent description than that obtained from traditional statistical methods based on integral spectral parameters (e.g., Hs, Tm, θm). Indeed, while the use of integral parameters allows a direct application of standard techniques for statistical analysis, important information related to the physics of the processes may be overlooked (e.g., the presence of multiple wave systems, for instance locally and remotely generated). The two proposed methods do not exclude integral parameters analysis, but they further allow accounting for different events (e.g., with different genesis) independently. Although SPS and SOM are equally valid for both numerical model and observational data, we illustrate their potential using a 37-year long (1979–2015) model dataset of directional wave spectra at a study site in the western Mediterranean Sea. We show that standard integral parameters fail to show the complex and even multimodal conditions at this site, that are otherwise revealed by the directional spectra statistical analysis. Although the processing pathways and the resulting indicators of both SPS and SOM are substantially different, we observe that their results are mutually consistent, and provide a better insight into the physical processes at work.

Spectral Signature of the Biosphere: NISTAR Finds It in Our Solar System From the Lagrangian L‐1 Point

AbstractNISTAR, aboard the DSCOVR spacecraft, is one of the National Aeronautics and Space Administration's energy budget instruments designed to measure the seasonal changes in Earth's total outgoing radiation from a unique vantage point at the Lagrangian L‐1 point a million miles from Earth. Global radiation energy balance measurements are important constraints for climate models, but are difficult measurements to quantify. CERES data offer the best current observational constraints, but need extensive modeling to get global energy. NISTAR observes the entire dayside hemisphere of the Earth as a single pixel, splitting the shortwave radiation into broadband visible and near‐infrared components (analogous to the narrowband spectral ratios used to define vegetation indices). This spectral partitioning at the 0.7‐μm vegetation red edge offers unique constraints on climate model spectral treatment of cloud and surface albedos. Moreover, NISTAR's unique viewing geometry amounts to observing the Earth as an exoplanet, which opens a new perspective on exoplanet observations.

A novel silicon(IV) phthalocyanine-oligopeptide conjugate as a highly efficient photosensitizer for photodynamic antimicrobial therapy

A novel silicon(IV) phthalocyanine (SiPc) axially di-substituted with Arg-Arg-Arg oligopeptides (compound 4) has been synthesized and characterized. Meanwhile, two analogues have been used as controls. The effect of arginine number on the spectral properties, photosensitizing activities, partition coefficient, cellular uptake, and photodynamic antimicrobial activities have been investigated. The novel SiPc 4 containing tri-arginine oligopeptides exhibits an extremely high in vitro photodynamic activity not only against Gram-positive/negative bacteria but also against fungus, which is obviously superior to its analogues containing mono-arginine (compound 2) and di-arginine oligopeptides (compound 3). In addition, 4 shows a higher photodynamic inactivation against S.aureus, E.coli and C. albicans cells than methylene blue (MB), a known antimicrobial photosensitizer. The IC90 values against C. albicans for 4 is ca. 179-fold lower than that for MB. Moreover, the potent photodynamic effect of 4 has been further confirmed by in vivo animal model. These results indicate that the tri-arginine-modified SiPc is a highly promising photosensitizer for photodynamic antimicrobial therapy.

Heavy hitters via cluster-preserving clustering

We develop a new algorithm for the turnstile heavy hitters problem in general turnstile streams, the EXPANDERSKETCH, which finds the approximate top- k items in a universe of size n using the same asymptotic O ( k log n ) words of memory and O (log n ) update time as the COUNTMIN and COUNTSKETCH, but requiring only O ( k poly (log n )) time to answer queries instead of the O ( n log n ) time of the other two. The notion of "approximation" is the same l 2 sense as the COUNTSKETCH, which given known lower bounds is the strongest guarantee one can achieve in sublinear memory. Our main innovation is an efficient reduction from the heavy hitters problem to a clustering problem in which each heavy hitter is encoded as some form of noisy spectral cluster in a graph, and the goal is to identify every cluster. Since every heavy hitter must be found, correctness requires that every cluster be found. We thus need a "cluster-preserving clustering" algorithm that partitions the graph into pieces while finding every cluster. To do this we first apply standard spectral graph partitioning, and then we use some novel local search techniques to modify the cuts obtained so as to make sure that the original clusters are sufficiently preserved. Our clustering algorithm may be of broader interest beyond heavy hitters and streaming algorithms.

Speech emotion recognition using emotion perception spectral feature

SummarySpeech emotion recognition is an important technique for human‐computer interface applications. Due to contain rich information of emotion, the spectral feature is widely used for emotion recognition. However, the recognition performance is limited because of imprecise extracted rule and uncertain size of resolution of spectral feature. To address this issue, motivated by speech coding, we introduced psychoacoustics model, provided a perception spectral subband partition method for obtaining more precise frequency resolution. Moreover, we also provided a new spectral feature on the divided subband frequency signals. The proposed feature includes emotional perception entropy, spectral inclination, and spectral flatness. Then, a Support Vector Machine classifier is used to recognize emotion categories. The experiment results show that the proposed spectral feature is superior to the traditional MFCC feature, and also better than the state‐of‐the‐art Fourier feature and multi‐resolution amplitude feature.

Spectral partitions for Sturm–Liouville problems

AbstractWe look for best partitions of the unit interval that minimize certain functionals defined in terms of the eigenvalues of Sturm–Liouville problems. Via Γ-convergence theory, we study the asymptotic distribution of the minimizers as the number of intervals of the partition tends to infinity. Then we discuss several examples that fit in our framework, such as the sum of (positive and negative) powers of the eigenvalues and an approximation of the trace of the heat Sturm–Liouville operator.