IJARAI Volume 4 Issue 8

Copyright Statement: This is an open access publication licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

View Full Issue

Paper 1: Wavelet Compressed PCA Models for Real-Time Image Registration in Augmented Reality Applications

Abstract: The use of augmented reality (AR) has shown great promise in enhancing medical training and diagnostics via interactive simulations. This paper presents a novel method to perform accurate and inexpensive image registration (IR) utilizing a pre-constructed database of reference objects in conjunction with a principal component analysis (PCA) model. In addition, a wavelet compression algorithm is utilized to enhance the speed of the registration process. The proposed method is used to perform registration of a virtual 3D heart model based on tracking of an asymmetric reference object. The results indicate that the accuracy of the method is dependent upon the extent of asymmetry of the reference object which required inclusion of higher order principal components in the model. A key advantage of the presented IR technique is the absence of a restart mechanism required by the existing approaches while allowing up to six orders of magnitude compression of the modeled image space. The results demonstrate that the method is computationally inexpensive and thus suitable for real-time augmented reality implementation.

Author 1: Christopher Cooper
Author 2: Kent Wise
Author 3: John Cooper
Author 4: Makarand Deo

Keywords: Image Registration; Principal Component Analysis; Wavelet Compression; Augmented Reality; Image Classification

PDF

Paper 2: Analysis and Prediction of Crimes by Clustering and Classification

Abstract: Crimes will somehow influence organizations and institutions when occurred frequently in a society. Thus, it seems necessary to study reasons, factors and relations between occurrence of different crimes and finding the most appropriate ways to control and avoid more crimes. The main objective of this paper is to classify clustered crimes based on occurrence frequency during different years. Data mining is used extensively in terms of analysis, investigation and discovery of patterns for occurrence of different crimes. We applied a theoretical model based on data mining techniques such as clustering and classification to real crime dataset recorded by police in England and Wales within 1990 to 2011. We assigned weights to the features in order to improve the quality of the model and remove low value of them. The Genetic Algorithm (GA) is used for optimizing of Outlier Detection operator parameters using RapidMiner tool.

Author 1: Rasoul Kiani
Author 2: Siamak Mahdavi
Author 3: Amin Keshavarzi

Keywords: crime; clustering; classification; genetic algorithm; weighting; rapidminer

PDF

Paper 3: Locality of Chlorophyll-A Distribution in the Intensive Study Area of the Ariake Sea, Japan in Winter Seasons based on Remote Sensing Satellite Data

Abstract: Mechanism of chlorophyll-a appearance and its locality in the intensive study area of the Ariake Sea, Japan in winter seasons is clarified by using remote sensing satellite data. Through experiments with Terra and AQUA MODIS data derived chlorophyll-a concentration and truth data of chlorophyll-a concentration together with meteorological data and tidal data which are acquired for 6 years (winter 2010 to winter 2015), it is found that strong correlation between the chlorophyll-a concentration and tidal height changes. Also it is found that the relations between ocean wind speed and chlorophyll-a concentration. Meanwhile, there is a relatively high correlation between sunshine duration a day and chlorophyll-a concentration. Furthermore, it is found that there are different sources of chlorophyll-a in the three different sea areas of Ariake Sea area in the back, Isahaya bay area, and Kumamoto offshore area.

Author 1: Kohei Arai

Keywords: chlorophyl-a concentration; red tide; diatom; solar irradiance; ocean winds; tidal effect

PDF

Paper 4: Appropriate Tealeaf Harvest Timing Determination Referring Fiber Content in Tealeaf Derived from Ground based Nir Camera Images

Abstract: Method for most appropriate tealeaves harvest timing with the reference to the fiber content in tealeaves which can be estimated with ground based Near Infrared (NIR) camera images is proposed. In the proposed method, NIR camera images of tealeaves are used for estimation of nitrogen content and fiber content in tealeaves. The nitrogen content is highly correlated to Theanine (amid acid) content in tealeaves. Theanine rich tealeaves taste good. Meanwhile, the age of tealeaves depend on fiber content. When tealeaves are getting old, then fiber content is increased. Tealeaf shape volume also is increased with increasing of fiber content. Fiber rich tealeaves taste not so good, in general. There is negative correlation between fiber content and NIR reflectance of tealeaves. Therefore, tealeaves quality of nitrogen and fiber contents can be estimated with NIR camera images. Also, the shape volume of tealeaves is highly correlated to NIR reflectance of tealeaf surface. Therefore, not only tealeaf quality but also harvest amount can be estimated with NIR camera images. Experimental results show the proposed method works well for estimation of appropriate tealeaves harvest timing with fiber content in the tealeaves in concern estimated with NIR camera images.

Author 1: Kohei Arai
Author 2: Yoshihiko Sasaki
Author 3: Shihomi Kasuya
Author 4: Hideto Matusura

Keywords: Tealeaves; Nitrigen content; Amino accid; Leaf volume; NIR images; Fiber content; Theanine; Amid acid; Regressive analysis

PDF

Paper 5: Driver’s Awareness and Lane Changing Maneuver in Traffic Flow based on Cellular Automaton Model

Abstract: Effect of driver’s awareness (e.g., to estimate the speed and arrival time of another vehicle) on the lane changing maneuver is discussed. “Scope awareness” is defined as the visibility which is required for the driver to make a visual perception about road condition and the speed of vehicle that appears in the target lane for lane changing in the road. Cellular automaton based simulation model is created and applied to simulation studies for driver awareness behavior. This study clarifies relations between the lane changing behavior and the scope awareness parameter that reflects driver behavior. Simulation results show that the proposed model is valid for investigation of the important features of lane changing maneuver.

Author 1: Kohei Arai
Author 2: Steven Ray Sentinuwo

Keywords: traffic cellular automata; scope awareness; lane changing maneuver; driver perception; speed estimation

PDF

Paper 6: Application of distributed lighting control architecture in dementia-friendly smart homes

Abstract: Dementia is a growing problem in societies with aging populations, not only for patients, but also for family members and for the society in terms of the associated costs of providing health care. Helping patients to maintain a degree of independence in their home environment while ensuring their safeties is considered as a positive step forward for addressing individual needs of dementia patients. A common symptom for dementia patients including those with Alzheimer’s Disease and Related Dementia (ADRD) is sleep disturbance, patients being awake at night and asleep during the day. One of the problems with night time sleep disturbance in dementia patients is the possible accidental falls of patients in the dark environment. An issue associated with un-hourly sleeping behavior in these patients is the lighting condition of their surroundings. Clinical studies indicate that appropriate level of lighting can help to restore the rest-activity cycles of ADRD patients. This study tackles this problem by generating machine learning solutions for controlling the lighting conditions of multiple rooms in the house in different hours based on patterns of behaviors generated for the patient. Several neural network oriented classification methods are investigated and their feasibilities are assessed with a collection of synthetic data capturing two conditions of balanced and unbalanced inter-class samples. The classifiers are utilized within two centric and distributed lighting control architectures. The results indicate the feasibility of the distributed architecture in achieving a high level of classification performance resulting in adequate control over lighting conditions of the house in various time periods.

Author 1: Atousa Zaeim
Author 2: Samia Nefti-Meziani
Author 3: Adham Atyabi

Keywords: Smart Home; Ambient intelligence; Machine Learn-ing; Distributed Learning

PDF