Kyoung-Gu Woo

An application-level energy-efficient scheduling for dynamic voltage and frequency scaling

Power consumption in mobile devices is a critical issue with monitoring-based services. Operating systems in smartphones employ interval-based dynamic voltage scaling algorithms to reduce power consumption. To boost the effect of those algorithms, we propose an application-level scheduling algorithm which slows down the execution of the application deliberately and thus maintains low utilization rate of CPU. The experimental result shows the proposed algorithm saves up to 32% of power consumption.

Fourier-based shape feature extraction technique for computer-aided B-Mode ultrasound diagnosis of breast tumor

Early detection of breast tumor is critical in determining the best possible treatment approach. Due to its superiority compared with mammography in its possibility to detect lesions in dense breast tissue, ultrasound imaging has become an important modality in breast tumor detection and classification. This paper discusses the novel Fourier-based shape feature extraction techniques that provide enhanced classification accuracy for breast tumor in the computer-aided B-mode ultrasound diagnosis system. To demonstrate the effectiveness of the proposed method, experiments were performed using 4,107 ultrasound images with 2,508 malignancy cases. Experimental results show that the breast tumor classification accuracy of the proposed technique was 15.8%, 5.43%, 17.32%, and 13.86% higher than the previous shape features such as number of protuberances, number of depressions, lobulation index, and dissimilarity, respectively.

A remote cardiac monitoring system for preventive care

Remote monitoring of heart disease patients has been shown to be effective for diagnosis and detection of arrhythmias. We propose a remote cardiac monitoring system for preventive care by developing a decision support system with personalized parameters and an algorithm to predict forthcoming paroxysmal atrial fibrillations. The system consists of several physiological measuring devices, mobile gateways, point-of-care devices, and a monitoring server. The proposed prediction algorithm shows 87.5% accuracy.

Adaptive logging for mobile device

Nowadays, due to the increased user requirements of the fast and reliable data management operation for mobile applications, major device vendors use embedded DBMS for their mobile devices such as MP3 players, mobile phones, digital cameras and PDAs. However, database logging is the major bottleneck against the fast response time. There has been a lot of work minimizing logging overhead but no single recovery method provides the best performance to a variety of database workloads. In this paper, we present a novel recovery method called adaptive logging which can switch the logging method from ARIES to shadow paging adaptively at a page level according to the update state of each page on run time. Also, we propose a log compaction method called deferred logging which removes redundant logs by deferring to create log records until the updated data page is flushed or until the transaction commits. Deferred logging is coupled with adaptive logging seamlessly so that it boosts the performance of adaptive logging by reducing the typical overhead of hybrid methods. We have implemented the proposed approaches to our embedded DBMS which was deployed to more than 10 million mobile devices and evaluated them through a real world application on a mobile device. The result shows that our approaches can reduce logging overhead significantly and consequently can improve the response time of both small update transaction and large update transaction effectively.

Efficient processing of substring match queries with inverted q-gram indexes

With the widespread of the internet, text-based data sources have become ubiquitous and the demand of effective support for string matching queries becomes ever increasing. The relational query language SQL also supports LIKE clause over string data to handle substring matching queries. Due to popularity of such substring matching queries, there have been a lot of study on designing efficient indexes to support the LIKE clause in SQL. Among them, q-gram based indexes have been studied extensively. However, how to process substring matching queries efficiently with such indexes has received very little attention until recently. In this paper, we show that the optimal execution of intersecting posting lists of q-grams for substring matching queries should be decided judiciously. Then we present the optimal and approximate algorithms based on cost estimation for substring matching queries. Performance study confirms that our techniques improve query execution time with q-gram indexes significantly compared to the traditional algorithms.

Multiobjective evolutionary optimization for tumor segmentation of breast ultrasound images

This paper proposes a robust multiobjective evolutionary algorithm (MOEA) to optimize parameters of tumor segmentation for ultrasound breast images. The proposed algorithm employs efficient schemes for reinforcing proximity to Pareto-optimal and diversity of solutions. They are designed to solve multiobjective problems for segmentation accuracy and speed. First objective is evaluated by difference between the segmented outline and ground truth. Second objective is evaluated by elapsed time during segmentation process. The experimental results show the effectiveness of the proposed algorithm compared with conventional MOEA from the viewpoint of proximity to the Pareto-optimal front (improved by 16.4% and 12.4%). Moreover, segmentation results of proposed algorithm describe faster segmentation speed (1.97 second) and higher accuracy (8% Jaccard).

Real-Time analysis of ECG data using mobile data stream management system

Monitoring and analyzing electrocardiogram(ECG) signals for the purpose of detecting cardiac arrhythmia is a challenging task, and often requires a Complex Event Processing (CEP) system to analyze real-time streamed data. Various server-based CEP engines exist today. However, they have practical limitations to be used in environments where network connectivity is poor and yet continuous real-time monitoring and analysis is critical. In this paper, we introduce a lightweight mobile-based CEP engine called Mobile Data Stream Management System (MDSMS) that runs on the smart phone. MDSMS is built on an extensible architecture with concepts such as lightweight scheduling and efficient tuple representation. MDSMS enables developers to easily incorporate domain specific functionalities with User Defined Operator (UDO) and User Defined Function (UDF). MDSMS also has other useful features, such as mechanisms for archiving streamed data in local or remote data stores. We also show effectiveness of our MDSMS by implementing a portable, continuous, and real-time cardiac arrhythmia detection system based on the MDSMS. The system consists of ECG sensor and a smart phone connected to each other via a wireless connection. MDSMS can detect and classify various arrhythmia conditions from ECG streams by executing arrhythmia detection algorithms written in Continuous Query Language.

Ultrasound breast lesion segmentation using adaptive parameters

In computer aided diagnosis for ultrasound images, breast lesion segmentation is an important but intractable procedure. Although active contour models with level set energy function have been proposed for breast ul- trasound lesion segmentation, those models usually select and x the weight values for each component of the level set energy function empirically. The xed weights might a ect the segmentation performance since the characteristics and patterns of tissue and tumor di er between patients. Besides, there is observer variability in probe handling and ultrasound machine gain setting. Hence, we propose an active contour model with adaptive parameters in breast ultrasound lesion segmentation to overcome the variability of tissue and tumor patterns between patients. The main idea is to estimate the optimal parameter set automatically for di erent input images. We used regression models using 27 numerical features from the input image and an initial seed box. Our method showed better results in segmentation performance than the original model with xed parameters. In addition, it could facilitate the higher classi cation performance with the segmentation results. In conclusion, the proposed active contour segmentation model with adaptive parameters has the potential to deal with various di erent patterns of tissue and tumor e ectively.

Non-rigid ultrasound image registration using generalized relaxation labeling process

This research proposes a novel non-rigid registration method for ultrasound images. The most predominant anatomical features in medical images are tissue boundaries, which appear as edges. In ultrasound images, however, other features can be identified as well due to the specular reflections that appear as bright lines superimposed on the ideal edge location. In this work, an image’s local phase information (via the frequency domain) is used to find the ideal edge location. The generalized relaxation labeling process is then formulated to align the feature points extracted from the ideal edge location. In this work, the original relaxation labeling method was generalized by taking n compatibility coefficient values to improve non-rigid registration performance. This contextual information combined with a relaxation labeling process is used to search for a correspondence. Then the transformation is calculated by the thin plate spline (TPS) model. These two processes are iterated until the optimal correspondence and transformation are found. We have tested our proposed method and the state-of-the-art algorithms with synthetic data and bladder ultrasound images of in vivo human subjects. Experiments show that the proposed method improves registration performance significantly, as compared to other state-of-the-art non-rigid registration algorithms.

Prediction of 4-year risk for coronary artery calcification using ensemble-based classification

The progression of coronary artery calcification (CAC) has been regarded as an important risk factor of coronary artery disease (CAD), which is the biggest cause of death. Because CAC occurrence increases the risk of CAD by a factor of ten, the one whose coronary artery is calcified should pay more attention to the health management. However, performing the computerized tomography (CT) scan to check if coronary artery is calcified as a regular examination might be inefficient due to its high cost. Therefore, it is required to identify high risk persons who need regular follow-up checks of CAC or low risk ones who can avoid unnecessary CT scans. Due to this reason, we develop a 4-year prediction model for a new occurrence of CAC based on data collected by the regular health examination. We build the prediction model using ensemble-based methods to handle imbalanced dataset. Experimental results show that the developed prediction models provided a reasonable accuracy (AUC 75%), which is about 5% higher than the model built by the other imbalanced classification method.