An Algorithm for Pattern Discovery in Time Series

We present a new algorithm for discovering patterns in time series and other sequential data. We exhibit a reliable procedure for building the minimal set of hidden, Markovian states that is statistically capable of producing the behavior exhibited in the data -- the underlying process's causal states. Unlike conventional methods for fitting hidden Markov models (HMMs) to data, our algorithm makes no assumptions about the process's causal architecture (the number of hidden states and their transition structure), but rather infers it from the data. It starts with assumptions of minimal structure and introduces complexity only when the data demand it. Moreover, the causal states it infers have important predictive optimality properties that conventional HMM states lack. We introduce the algorithm, review the theory behind it, prove its asymptotic reliability, use large deviation theory to estimate its rate of convergence, and compare it to other algorithms which also construct HMMs from data. We also illustrate its behavior on an example process, and report selected numerical results from an implementation.Comment: 26 pages, 5 figures; 5 tables; http://www.santafe.edu/projects/CompMech Added discussion of algorithm parameters; improved treatment of convergence and time complexity; added comparison to older method

Local Line Binary Pattern for Feature Extraction on Palm Vein Recognition

In recent years, palm vein recognition has been studied to overcome problems in conventional systems in biometrics technology (finger print, face, and iris). Those problems in biometrics includes convenience and performance. However, due to the clarity of the palm vein image, the veins could not be segmented properly. To overcome this problem, we propose a palm vein recognition system using Local Line Binary Pattern (LLBP) method that can extract robust features from the palm vein images that has unclear veins. LLBP is an advanced method of Local Binary Pattern (LBP), a texture descriptor based on the gray level comparison of a neighborhood of pixels. There are four major steps in this paper, Region of Interest (ROI) detection, image preprocessing, features extraction using LLBP method, and matching using Fuzzy k-NN classifier. The proposed method was applied on the CASIA Multi-Spectral Image Database. Experimental results showed that the proposed method using LLBP has a good performance with recognition accuracy of 97.3%. In the future, experiments will be conducted to observe which parameter that could affect processing time and recognition accuracy of LLBP is neede

Comparative Study of Rtos and Primitive Interrupt in Embedded System

Multitasking is one of the most challenging issues in the automation industry which is highly depended on the embedded system. There are two methods to perform multitasking in embedded system: RTOS and primitive interrupt. The main purpose of this research is to compare the performance of R¬TOS with primitive method while concurrently undertaking multiple tasks. The system, which is able to perform various tasks, has been built to evaluate the performance of both methods. There are four tasks introduced in the system: servo task, sensor task, LED task, and LCD task. The performance of each method is indicated by the success rate of the sensor task detection. Sensor task detection will be compared with the true value which is calculated and measured manually during observation time. Observation time was varied after several iterations and the data of the iteration are recorded for both RTOS and primitive interrupt methods. The results of the conducted experiments have shown that, RTOS is more accurate than interrupt method. However, the data variance of the primitive interrupt method is narrower than RTOS. Therefore, to choose a better method, an optimization is needed to be done and each product has its own standard

Coverage, Diversity, and Coherence Optimization for Multi-document Summarization

A great summarization on multi-document with similar topics can help users to get useful in¬for¬ma¬tion. A good summary must have an extensive coverage, minimum redundancy (high diversity), and smooth connection among sentences (high coherence). Therefore, multi-document summarization that con¬siders the coverage, diversity, and coherence of summary is needed. In this paper we pro¬pose a novel method on multi-document summarization that optimizes the coverage, diversity, and co¬her¬ence among the summary's sentences simultaneously. It integrates self-adaptive differential evo¬lu¬tion (SaDE) al¬gorithm to solve the optimization problem. Sentences ordering algorithm based on top¬ic¬al closeness ap¬proach is performed in SaDE iterations to improve coherences among the summary's sen¬tences. Ex¬pe¬ri¬ments have been performed on Text Analysis Conference (TAC) 2008 data sets. The ex¬perimental re¬sults showed that the proposed method generates summaries with average coherence and ROUGE scores 29-41.2 times and 46.97-64.71% better than any other method that only consider coverage and di¬versity, re-spect¬ive¬ly

Image Splicing Detection Based on Demosaicking and Wavelet Transformation

Image splicing is a form of digital image manipulation by combining two or more image into a new image. The application was developed through a passive approach using demosaicking and wavelet transformation method. This research purposed a method to implement the demosaicking and wavelet transform for digital image forgery detection with a passive approach. This research shows that (1) demosaicking can be used as a comparison image in forgery detection; (2) the application of demosaicking and wavelet transformation can improve the quality of the input image (3) demosaicking and wavelet algorithm are able to estimate whether the input image is real or fake image with a passive approach and estimate the manipulation area from the input image

Studi Perbandingan Back Propogation

Keberhasilan pemahaman tentang bagaimana membuat komputer belajar akan membuka banyak manfaat baru dari komputer. Sebuah pemahaman yang rinci tentang algoritma pengolahan informasi untuk pembelajaran mesin dapat membuat pemahaman yang sebaik kemampuan belajar manusia. Banyak jenis pembelajaran mesin yang kita tahu, beberapa diantaranya adalah Backpropagation (BP), Extreme Learning Machine (ELM), dan Support Vector Machine (SVM). Penelitian ini menggunakan lima data yang memiliki beberapa karakteristik. Hasil penelitian ini, dari ketiga model yang diamati memberikan akurasi klasifikasi yang sebanding. Penelitian ini memiliki tiga kesimpulan, yang terbaik dalam akurasi adalah BP, yang terbaik dalam stabilitas adalah SVM dan CPU time terbaik adalah ELM untuk data bioinformatika

A Change Detection and Resource-aware Data Sensing Approaches for Improving the Reporting Protocol Mechanism for Mobile User

Update mechanism is an important process that relays information to the end-user by sending the data from the client to the server. There are several kinds of update mechanism that are used, one of them is reporting protocol. Reporting protocol sends the data from the client to the server continuously in a certain time interval. Reporting protocol occasionally sends the same information repeatedly to the end-user and sometimes the data aren’t needed by the end-user. This is an issue, because it can cause a large amount of bandwidth USAge. In this research, we have developed an improvement of the reporting protocol mechanism for mobile user using change detection and resource-aware data sensing to minimize the bandwidth and resource USAge. The improvement of reporting protocol that is implemented reduces frequency of data transfer with the prediction of the changes in user activity and position. The prediction is used as a trigger when the data is about to be sent. The results have shown that the adaptive reporting protocol could improve the performance of the overall reporting protocol. This is shown by the improvement of the bandwidth efficiency up to 36-97%, memory efficiency at 1.5-6% and battery efficiency at 7-13%