JICE 2015 December

JICE December 2015 academic PDFs

Showing 1–8 of 10 results

  • Robust Keypoint Detection against Affine Transformation Using Moment Invariants on Intrinsic Mode Function


    Abstract: Scale Invariant Feature Transform (SIFT) is a method to detect and match invariant feature points on images, and is robust against contrast, rotation, and scale changes. However, SIFT cannot find many correct matching points between affine transformed images because this method employs Gaussian function for scale parameter which specifies a circle area on image planes. In this paper, we propose a method using Bi-dimensional Empirical Mode Decomposition (BEMD) for keypoint detection, where a given image is decomposed into Intrinsic Mode Functions (IMFs). Our method also employs Affine Moment Invariants (AMIs) instead of SIFT’s feature values. As a result, the proposed method detects more matching points than SIFT in a steep affine
    transformed image.
    Keywords: Empirical Mode Decomposition, Affine Moment Invariants.

  • Edge Reconstruction of LED Probes Using Various Segmentation and the Averaging of Sub-pixels


    Abstract: LED probes are essential for testing the quality of LEDs, gaining its attention among industrial applications. Disturbance factors such as dust or noise affections may occur during the manufacturing process of the LED probes, which leads angle error to increase. With the increasing demand for LED probes, higher precision and efficiency are expected by users. Efficient method for edge detection and the preciseness of angle is crucial in our study. The previous study presented a method using Scharr edge detection and adaptive reconstruction. In this paper, we add a new method based on various segmentation and the averaging of sub-pixels (VSAS). Experimental results indicate that this method provides higher precision, with and an average error less than 1% compared to the other previous methods.
    Keywords: Angle accuracy, edge detection, led, probe, sub-pixels, various segmentation

  • Multiscale Feature Representation for ECG-based Human Identification


    Abstract: ECG-based based human recognition is increasingly becoming a popular modality for
    biometric authentication. Two important features of ECG signals are liveliness and the robustness
    against falsification. However, ECG features vary due to muscle flexure, baseline wander, and other sources of noise. This paper presents a new method which extracts multiscale geometric features from ECG signals and apply them for human identification. A non-linear filter is applied for preprocessing the ECG signal. The refined ECG signal is then divided into multiple segments and feature matrix is computed by multiscale pattern extraction technique. Feature matrix is finally applied to a simple minimum distance to mean classifier adopting leave-one-out procedure. An experiment with 60 ECG signals from 60 subjects shows promising performance of the proposed method compared to the conventional ECG features.
    Keywords: Binary patterns, multiscale representation, supervised classification, and human

  • A Novel Recognition System for Digits Writing in the Air Using Coordinated Path Ordering


    Abstract: With the invention of Microsoft Kinect sensor, human-computer interaction is gaining its
    attention and becoming available for widespread use. The previous study presented a method of
    Kinect-based mid-air handwritten digit recognition for Android smart phones with a recognition
    accuracy of only about 94.6%. In this paper, we propose an improved method based on the
    normalizing and scaling of path order coordinates. With that, the proposed method leads to accuracy elevation and executing time reduction. Experimental results show an average recognition accuracy rate of 96.8% was achieved for each number.
    Keywords: Accuracy rate, handwritten digit recognition, human-computer interaction, Kinect sensor support vector machine

  • Path Detection in Virtual Environment for Synchronous EEG by Density Based Support Vector Machine


    Abstract: The use of Brain-Computer Interface (BCI) has been increasing exponentially in the recent years due to the use of low-cost commercial Fast Fourier Transform (FFT) based EEG reading devices with nonclinical accuracy for consumer application development. Also, the design and implementation of 3D virtual environments for BCI training purposes has proven to be effective due to the high interaction with the end user and the assistance for recreating a specific type of signal or behavior. The aim of this paper is to present a method and the results of applying a binary Density Based Support Vector Machine (DBSVM) Classifier in a 3D virtual environment designed for interaction with EEG predefined signal patterns. The environment trains the classifier by taking 180 second EPOCHs and classifying them into a successful/unsuccessful attempt per test subject. The applications can be extended for implementing mind-wave pattern password or tracing a specific set of mind-based commands for virtual path tracing purposes. The tested SVM had a success rate of 60%. Further work includes the study of different classifier features and implementation of a dynamic classifier.
    Keywords: BMI, MindWave, SVM, Pattern Recognition, Machine Learning

  • Electrical impedance imaging based on ultrasonic B-mode image


    Abstract: This paper presents electrical impedance imaging based on ultrasonic B-mode image for
    detecting breast tumor. In the proposed system, a thin film with 16 electrodes was pasted on the
    surface of the ultrasound probe. The proposed system allows both the electrical conductivity image by sixteen film electrodes and ultrasound image by the ultrasonic probe at same area. The electrical conductivity image is improved by using ultrasound image as the prior information. In the simulation and experiment, the electrical conductivity distribution at the simple breast model was reconstructed by the proposed system. The comparison between the electrical images with and without the ultrasound image as the prior information was presented. As results, it was found that the electrical conductivity distribution was clearly improved by using ultrasonic image as the prior information although the proposed system must be improved.
    Keywords: Ultrasonic imaging, electrical impedance imaging, linear array sensor

  • Construction of Hydraulically Balanced Water Distribution Network


    Abstract: This paper represents creating and analyzing of an adequate hydraulically balanced Water Distribution Network (WDN) based on Geographical Information System (GIS) and Epanet softwares. Fist a proper and functional WDN has been built and skeletonized by network analyst methodology in ArcGIS. Then the project scenario has been imported into Epanet with simple text or Epanet’s readable format consisted of multiple physical or non-physical WDN objects and its characteristics. These all in order to determine analyze and simulate hydraulic parameters of WDN. Second, we analyzed and determined water flows, water in each pipe, water flows direction as well as pressure heads at each node in the network with other required hydraulics parameters. The advantages of this method are simple math and self-correction.
    Concisely, the context of this work is to use GIS and Epanet-based methodologies as well as
    solution of closed-loop network problems. Used continuity and energy conservation methods due to determination of pressure heads at each node, water flows and water flows direction at each pipe in the network. As a result got a satisfied hydraulically balanced and a systematic WDN appropriately as the implications of this paper approaches consequently.
    Keywords: Geographical Information System (GIS); Water Distribution Network (WDN), Hydraulic,

  • Homotopic parts configuration management using the cellular data system


    Abstract: Lacking proper theory and design, big data has continued to grow in a chaotic way and are now beyond human recognition and control. To address this, we have been researching an application of the concept of homotopy preservation in homotopy type theory to remove the bottleneck in big data processing, originating from combinatorial explosion of information, which makes use of the seven layers, from homotopy to presentation, of the incrementally modular abstraction hierarchy (IMAH).
    We developed the Cellular Data System (CDS) as an implementation of the IMAH in our previous
    work. In this paper, we introduce a position information formula, which identifies the relative position of a specified factor in a formula in CDS. This function has an important role for preserving homotopy in actual changes of objects. We show the effectiveness of the formula by taking up a case study of component configuration information management in manufacturing. In the case study, it is demonstrated that design component configuration information and manufacturing component configuration information are consistently managed by preserving homotopy, even when component configuration frequently changes.
    Keywords: Position information formula, Component configuration information management,
    Incrementally Modular Abstraction Hierarchy, Cellular Data System