Multilevel Pattern Mining Architecture for Automatic Network Monitoring in Heterogeneous Wireless Communication Networks

The rapid development of network technology and its evolution toward heterogeneous networks has increased the demand to support automatic monitoring and the management of heterogeneous wireless communication networks.This paper presents a multilevel pattern mining architecture to support automatic network management by discovering interesting patterns from telecom network monitoring data.This architecture leverages and combines existing frequent itemset discovery over data streams,association rule deduction,frequent sequential pattern mining,and frequent temporal pattern mining techniques while also making use of distributed processing platforms to achieve high-volume throughput.

A virtual logic analyzer implemented with Arduino

A simple,stable and reliable virtual logic analyzer is presented. The logic analyzer had two modules:one was the test pattern generation module,the other was the logic monitoring module. Combining the two modules,one is able to test a digital circuit automatically. The user interface of the logic analyzer was programmed with LabVIEW. Two Arduino UNO boards were used as the hardware targets to input and output the logic signals. The maximum pattern update rate was set to be 20 Hz. The maximum logic sampling rate was set to be 200 Hz. After twelve thousand cycles of exhaustive tests,the logic analyzer had a 100% accuracy. As a tutorial showing how to build virtual instruments with Arduino,the software detail is also explained in this article.

Automated Burned Scar Mapping Using Sentinel-2 Imagery

The Sentinel-2 satellites are providing an unparalleled wealth of high-resolution remotely sensed information with a short revisit cycle, which is ideal for mapping burned areas both accurately and timely. However, the high detail and volume of information provided actually encumbers the automation of the mapping process, at least for the level of automation required to map systematically wildfires on a national level. This paper proposes a fully automated methodology for mapping burn scars using Sentinel-2 data. Information extracted from a pair of Sentinel-2 images, one pre-fire and one post-fire, is jointly used to automatically label a set of training patterns via two empirical rules. An initial pixel-based classification is derived using this training set by means of a Support Vector Machine (SVM) classifier. The latter is subsequently smoothed following a multiple spectral-spatial classification (MSSC) approach, which increases the mapping accuracy and thematic consistency of the final burned area delineation. The proposed methodology was tested on six recent wildfire events in Greece, selected to cover representative cases of the Greek ecosystems and to present challenges in burned area mapping. The lowest classification accuracy achieved was 92%, whereas Matthews correlation coefficient (MCC) was greater or equal to 0.85.

Automatic acquisition of pattern collocations in GO

The quality, quantity, and consistency of the knowledge used in GO-playing programs often determine their strengths, and automatic acquisition of large amounts of high-quality and consistent GO knowledge is crucial for successful GO playing. In a previous article of this subject, we have presented an algorithm for efficient and automatic acquisition of spatial patterns of GO as well as their frequency of occurrence from game records. In this article, we present two algorithms, one for efficient and automatic acquisition of pairs of spatial patterns that appear jointly in a local context, and the other for deter- mining whether the joint pattern appearances are of certain significance statistically and not just a coincidence. Results of the two algorithms include 1 779 966 pairs of spatial patterns acquired automatically from 16 067 game records of professsional GO players, of which about 99.8% are qualified as pattern collocations with a statistical confidence of 99.5% or higher.

Automatic pattern acquisition from game records in GO

Computer programs of GO are typically constructed using a knowledge-based approach with heuristics and pattern matching because of enormous complexities of the game. In this approach, quantity, quality, and consistency of patterns used in computer programs of GO to a large extent determine the strengths of the programs. This study presents an effective method to acquire automatically comprehensive GO patterns from large collections of game records. Statistical usages of the patterns ensure consistency and quality of the patterns, which in turn can help improve the strengths of computer GO programs. Additionally, statistical usages of patterns from different sources of game records clearly show subtle and significant discrepancies among various types of GO players, and clarify certain myths in the playing of GO.

Simulation of ATPG Neural Network and Its Experimental Results

This paper first establishes a neural network model for logic circuits fromthe truth table by using linear equations theory, presents a kind of ATPG neuralnetwork model, and investigates energy local minima for the network- And then,it proposes the corresponding techniques to reduce the number of energy localminima as well as some approaches to escaping from local minimum of eliergyFinally, two simulation systems, the binary ATPG neural network and thecontinuous ATPG neural network, are implemented oli SUN 3/260 workstationin C language. The experimental results and their analysis and discussion aregiven. The preliminary experimental results show that this method is feasibleand promising.

Efficient Static Compaction of Test Patterns Using Partial Maximum Satisfiability

Static compaction methods aim at finding unnecessary test patterns to reduce the size of the test set as a post-process of test generation.Techniques based on partial maximum satisfiability are often used to track many hard problems in various domains,including artificial intelligence,computational biology,data mining,and machine learning.We observe that part of the test patterns generated by the commercial Automatic Test Pattern Generation(ATPG)tool is redundant,and the relationship between test patterns and faults,as a significant information,can effectively induce the test patterns reduction process.Considering a test pattern can detect one or more faults,we map the problem of static test compaction to a partial maximum satisfiability problem.Experiments on ISCAS89,ISCAS85,and ITC99 benchmarks show that this approach can reduce the initial test set size generated by TetraMAX18 while maintaining fault coverage.