An exact method for graph coloring

We are interested in the graph coloring problem. We propose an exact method based on a linear-decomposition of the graph. The complexity of this method is exponential according to the linearwidth of the entry graph, but linear according to its number of vertices. We present some experiments performed on literature instances, among which COLOR02 library instances. Our method is useful to solve more quickly than other exact algorithms instances with small linearwidth, such as mug graphs. Moreover, our algorithms are the first to our knowledge to solve the COLOR02 instance 4-Inser_3 with an exact method.     

雷达接收机多通道高速数据采集系统

介绍了一种用于雷达接收机的多通道高速数据采集系统。该系统以计算机为采集主控单元,兼容多种格式的数据,可以实现多通道的循环采集。系统采集单元由可编程逻辑器件实现,简化了电路结构,增强了系统设计的灵活性和可靠性,降低了体积与成本。实测结果表明该系统工作稳定、性能良好,目前已应用于多个型号的雷达接收机中。     

Analysis of classification accuracy for pre-filtered multichannel remote sensing data

Noise is one of the main factors degrading the quality of original multichannel remote sensing data and its presence influences classification efficiency, object detection, etc. Thus, pre-filtering is often used to remove noise and improve the solving of final tasks of multichannel remote sensing. Recent studies indicate that a classical model of additive noise is not adequate enough for images formed by modern multichannel sensors operating in visible and infrared bands. However, this fact is often ignored by researchers designing noise removal methods and algorithms. Because of this, we focus on the classification of multichannel remote sensing images in the case of signal-dependent noise present in component images. Three approaches to filtering of multichannel images for the considered noise model are analysed, all based on discrete cosine transform in blocks. The study is carried out not only in terms of conventional efficiency metrics used in filtering (MSE) but also in terms of multichannel data classification accuracy (probability of correct classification, confusion matrix). The proposed classification system combines the pre-processing stage where a DCT-based filter processes the blocks of the multichannel remote sensing image and the classification stage. Two modern classifiers are employed, radial basis function neural network and support vector machines. Simulations are carried out for three-channel image of Landsat TM sensor. Different cases of learning are considered: using noise-free samples of the test multichannel image, the noisy multichannel image and the pre-filtered one. It is shown that the use of the pre-filtered image for training produces better classification in comparison to the case of learning for the noisy image. It is demonstrated that the best results for both groups of quantitative criteria are provided if a proposed 3D discrete cosine transform filter equipped by variance stabilizing transform is applied. The classification results obtained for data pre-filtered in different ways are in agreement for both considered classifiers. Comparison of classifier performance is carried out as well. The radial basis neural network classifier is less sensitive to noise in original images, but after pre-filtering the performance of both classifiers is approximately the same.     

An effective heuristic algorithm for sum coloring of graphs

Given an undirected graph G=(V,E), the minimum sum coloring problem (MSCP) is to find a legal vertex coloring of G, using colors represented by natural numbers (1,2,…) such that the total sum of the colors assigned to the vertices is minimized. In this paper, we present EXSCOL, a heuristic algorithm based on independent set extraction for this NP-hard problem. EXSCOL identifies iteratively collections of disjoint independent sets of equal size and assign to each independent set the smallest available color. For the purpose of computing large independent sets, EXSCOL employs a tabu search based heuristic. Experimental evaluations on a collection of 52 DIMACS and COLOR2 benchmark graphs show that the proposed approach achieves highly competitive results. For more than half of the graphs used in the literature, our approach improves the current best known upper bounds.     

An exact algorithm with learning for the graph coloring problem

Given an undirected graph G=(V,E), the Graph Coloring Problem (GCP) consists in assigning a color to each vertex of the graph G in such a way that any two adjacent vertices are assigned different colors, and the number of different colors used is minimized. State-of-the-art algorithms generally deal with the explicit constraints in GCP: any two adjacent vertices should be assigned different colors, but do not specially deal with the implicit constraints between non-adjacent vertices implied by the explicit constraints. In this paper, we propose an exact algorithm with learning for GCP which exploits the implicit constraints using propositional logic. Our algorithm is compared with several exact algorithms among the best in the literature. The experimental results show that our algorithm outperforms other algorithms on many instances. Specifically, our algorithm allows to close the open DIMACS instance 4-Fullins_5.     

An improved algorithm for online coloring of intervals with bandwidth

We present an improved online algorithm for coloring interval graphs with bandwidth. This problem has recently been studied by Adamy and Erlebach and a 195-competitive online strategy has been presented. We improve this by presenting a 10-competitive strategy. To achieve this result, we use variants of an optimal online coloring algorithm due to Kierstead and Trotter.     

A data mining algorithm for automated characterisation of fluctuations in multichannel timeseries

We present a data mining technique for the analysis of multichannel oscillatory timeseries data and show an application using poloidal arrays of magnetic sensors installed in the H-1 heliac. The procedure is highly automated, and scales well to large datasets. The timeseries data is split into short time segments to provide time resolution, and each segment is represented by a singular value decomposition (SVD). By comparing power spectra of the temporal singular vectors, related singular values are grouped into subsets which define fluctuation structures. Thresholds for the normalised energy of the fluctuation structure and the normalised entropy of the SVD can be used to filter the dataset. We assume that distinct classes of fluctuations are localised in the space of phase differences Δψ(n,n+1) between each pair of nearest neighbour channels. An expectation maximisation clustering algorithm is used to locate the distinct classes of fluctuations and assign mode numbers where possible, and a cluster tree mapping is used to visualise the results.     

Monitoring global and local variations in multichannel functional data for manufacturing processes

This study models global and local variations hidden in multichannel functional data (MFD) for the purpose of manufacturing process monitoring. With advances in sensing technology, online measurement of manufacturing process variables could take the shape of multichannel curves. Although MFD contains rich information about process conditions, it is a challenging issue to model and interpret complex variations in MFD for process change detection and process faulty condition discrimination. A new approach was developed in this paper to decompose each channel of functional data into global and local variation components. Based on the extracted patterns, a principal curve regression method was applied to detect and discriminate different process conditions. The method was validated by real data from a forging plant. A simulation study was also conducted to verify the approach for MFD with complex patterns.     

Dementia classification using MR imaging and clinical data with voting based machine learning models

Dementia is one of the leading causes of severe cognitive decline, it induces memory loss and impairs the daily life of millions of people worldwide. In this work, we consider the classification of dementia using magnetic resonance (MR) imaging and clinical data with machine learning models. We adapt univariate feature selection in the MR data pre-processing step as a filter-based feature selection. Bagged decision trees are also implemented to estimate the important features for achieving good classification accuracy. Several ensemble learning-based machine learning approaches, namely gradient boosting (GB), extreme gradient boost (XGB), voting-based, and random forest (RF) classifiers, are considered for the diagnosis of dementia. Moreover, we propose voting-based classifiers that train on an ensemble of numerous basic machine learning models, such as the extra trees classifier, RF, GB, and XGB. The implementation of a voting-based approach is one of the important contributions, and the performance of different classifiers are evaluated in terms of precision, accuracy, recall, and F1 score. Moreover, the receiver operating characteristic curve (ROC) and area under the ROC curve (AUC) are used as metrics for comparing these classifiers. Experimental results show that the voting-based classifiers often perform better compared to the RF, GB, and XGB in terms of precision, recall, and accuracy, thereby indicating the promise of differentiating dementia from imaging and clinical data.

     

Clutter suppression and moving target imaging approach for multichannel hypersonic vehicle borne radar

In this paper, a clutter suppression and ground moving target imaging algorithm is proposed for the hypersonic vehicle (HSV) borne multichannel synthetic aperture radar (SAR) system. Different from the traditional horizontal flight radar platforms, the HSV-borne radar platform is with a complex movement, and typically has a skipping trajectory. Therefore, the clutter suppression and imaging operations are difficult to implement. To deal with this problem, a descending stage signal model of multichannel SAR system is presented and studied for the HSV-borne radar. Then, the clutter and moving target echoes are transformed into the azimuth frequency domain. Considering that there exists a phase difference between clutter and the moving target, the clutter suppression approach is implemented with a series of spatial domain filters. After that, the moving target is focused based on the keystone transform. In addition, the performance analyses and related issues of the proposed method are presented. Finally, some simulation experiments are taken to validate the effectiveness of the proposed method.     

高性能同时采样ADC在电力线监控系统中的应用

阐述了MAX11046等同时采样ADC在电力线监测中的应用,介绍了交流电测量的基本原理,给出了电网监测的典型应用以及国际标准对监测系统的要求.归纳了MAX11046电力线监测的独特优势并提供了一个应用案例,介绍如何选择外围器件,优化系统性能.     

An inexpensive microprocessor-controlled multichannel scaling system for in vivo radiotracer studies

A multichannel scaling (MCS) system has been developed to monitor the biological distribution of radiotracers in vivo. The design of this system permits the simultaneous collection of data from up to four radiation detectors. The evolution of detected count rates is displayed in real time as a histogram with a minimum channel width of 1 s. Each point in the data arrays can accommodate more than 16 million counts. The low-power Schottky integrated circuits used can readily process the maximum count rates that sodium iodide detectors can produce. The four counting units together with control circuitry were constructed on a single custom IBM PC expansion board.     

An interference-aware multichannel media access control protocol for wireless sensor networks

Scalable Parallel Systems (SPS) have offered a challenging model of computing and poses fascinating optimizations in sensor networks. With the development of sensor hardware technology, a certain sensor node is equipped with a radio transceiver that can be tuned to work on multiple channels. In this paper, we develop a novel interference-aware multichannel media access control (IMMAC) protocol for wireless sensor networks, which takes advantage of multichannel availability. Firstly, each node is assigned with a quiescent channel to reduce hidden terminal beforehand, and then it makes channel adjustment according to dynamic traffic. Secondly, a scalable multichannel media access control protocol is designed to make a tradeoff between channel switching overhead and fairness, and it effectively supports for node unicast and broadcast based on the receiver-directed channel switching. We have implemented simulation to evaluate the performance of IMMAC by comparing with other relevant protocols. The results show that our protocol exhibits more prominent ability, which utilizes multichannel to make parallel transmission and reduce hidden terminal problems effectively in resource-constrained wireless sensor networks.     

A 12-bit multichannel ADC for pixel detectors in particle physics and nuclear imaging

Modern pixel detectors in nuclear and particle physics experiments and also in nuclear imaging,starve for highly integrated application specified integrated circuit(ASIC),whereas in China the study of ASIC still stays far away from practical application.The lack of ASIC strictly limits the research and development of domestic high energy physics field.A 12-bit multichannel ADC designed for high density readout is introduced as a major candidate for solution.A precise model is discussed and the simulation fu...     

Classification by progressive generalization: A new automated methodology for remote sensing multichannel data

A new procedure for digital image classification is described. The procedure, labelled Classification by Progressive Generalization (CPG), was developed to avoid drawbacks associated with most supervised and unsupervised classifications. Using lessons from visual image interpretation and map making, non-recursive CPG aims to identify all significant spectral clusters within the scene to be classified. The basic principles are: (i) initial data compression using spectral and spatial techniques; (ii) identification of all potentially significant spectral clusters in the scene to be classified; (iii) minimum distance classification; and (iv) the use of spectral, spatial and large-scale pattern information in the progressive merging of the increasingly dissimilar clusters. The procedure was tested with high- (Landsat Thematic Mapper (TM)) and medium- (Advanced Very High Resolution Radiometer (AVHRR) 1 km composites) resolution data. It was found that the CPG yields classification accuracies comparable to, or better than, current unsupervised classification methods, is less sensitive to control parameters than a commonly used unsupervised classifier, and works well with both TM and AVHRR data. The CPG requires only three parameters to be specified at the outset, all specifying sizes of clusters that can be neglected at certain stages in the process. Although the procedure can be run automatically until the desired number of classes is reached, it has been designed to provide information to the analyst at the last stage so that final cluster merging decisions can be made with the analyst's input. It is concluded that the strategy on which the CPG is based provides an effective approach to the classification of remote sensing data. The CPG also appears to have a considerable capacity for data compression.     

A new approach for determining the spectral data of multichannel harmonic signals in noise

In this paper we present a new recursive procedure for identifying both the frequencies and amplitude matrices corresponding to a multichannel harmonic signal in additive correlated noise. The procedure follows from a complete characterization of the eigenstructure of the adjoint operator of the Naimark dilation for the observation correlation sequence. This research was supported in part by the Research Fund of Indiana University and the Natural Science Foundation under Grant No. ECS 841935.     

An evolutionary approach with diversity guarantee and well-informed grouping recombination for graph coloring

We present a diversity-oriented hybrid evolutionary approach for the graph coloring problem. This approach is based on both generally applicable strategies and specifically tailored techniques. Particular attention is paid to ensuring population diversity by carefully controlling spacing among individuals. Using a distance measure between potential solutions, the general population management strategy decides whether an offspring should be accepted in the population, which individual needs to be replaced and when mutation is applied. Furthermore, we introduce a special grouping-based multi-parent crossover operator which relies on several relevant features to identify meaningful building blocks for offspring construction. The proposed approach can be generally characterized as “well-informed”, in the sense that the design of each component is based on the most pertinent information which is identified by both experimental observation and careful analysis of the given problem. The resulting algorithm proves to be highly competitive when it is applied on the whole set of the DIMACS benchmark graphs.     

Clustering of periodic multichannel timeseries data with application to plasma fluctuations

A periodic datamining algorithm has been developed and used to extract distinct plasma fluctuations in multichannel oscillatory timeseries data. The technique uses the Expectation Maximisation algorithm to solve for the maximum likelihood estimates and cluster assignments of a mixture of multivariate independent von Mises distributions (EM-VMM). The performance of the algorithm shows significant benefits when compared to a periodic k-means algorithm and clustering using non-periodic techniques on several artificial datasets and real experimental data. Additionally, a new technique for identifying interesting features in multichannel oscillatory timeseries data is described (STFT-clustering). STFT-clustering identifies the coincidence of spectral features over most channels of a multi-channel array using the averaged short time Fourier transform of the signals. These features are filtered using clustering to remove noise. This method is particularly good at identifying weaker features and complements existing methods of feature extraction. Results from applying the STFT-clustering and EM-VMM algorithm to the extraction and clustering of plasma wave modes in the time series data from a helical magnetic probe array on the H-1NF heliac are presented.     

Learning classifier system for graph coloring

In this paper, the use of genetic algorithms and classifiers systems in the graph coloring application was considered. A crossover derivative was developed to accommodate the graph coloring specifications. Also, a method for verifying and validating the new offspring coloring rules and regulations was considered. Through a series of experiments conducted on randomly generated graphs of a relatively small size, the importance of various parameters and their impact to the overall system performance is demonstrated and discussed. In conclusion, suggestions for future study regarding this topic are proposed and briefly discussed.