Jinseok Chae

Experiments for a Collaborative Haptic Virtual Reality

In this paper, we present preliminary results of our ongoing networked virtual reality project by discussing the implementation and performance of a networked haptic game. In this game, online players feel like handling a real basketball since they are able to feel the sense of touch by using haptic interfaces. One of challenging issues in this implementation is haptic data transmission over the Internet to allow online multi-user play. Since haptic information is extremely sensitive to delay and jitter, the provision of timely transmission is critical. We carry out experiments to compare the performance of the haptic data transmission when using UDP (User Datagram Protocol) and TCP (Transmission Control Protocol), and observe that neither UDP nor TCP is able to satisfy QoS (Quality of Service) requirements of the haptic transmission. As a further study, we plan to investigate some alternative haptic data transmission schemes.

A Two-Phase Stock Trading System Using Distributional Differences

In the context of a dynamic trading environment, the ultimate goal of the financial forecasting system is to optimize a specific trading objective. This paper presents a two-phase (extraction and filtering) stock trading system that aims at maximizing the rates of returns. Extraction of stocks is performed by searching specific time-series patterns described by a combination of values of technical indicators. In the filtering phase, several rules are applied to the extracted sets of stocks to select stocks to be actually traded. The filtering rules are induced from past data using distributional differences. From a large database of daily stock prices, the values of technical indicators are calculated. They are used to make the extraction patterns, and the distributions of the discretization intervals of the values are calculated for both positive and negative data sets. The values in the intervals of distinctive distribution may contribute to the prediction of future trend of stocks, so the rules for filtering stocks are induced using those intervals. We show the rates of returns by the proposed trading system, with the usefulness of the rule induction method using distributional differences.

Quantum-Based Fixed Priority Scheduling

Fixed priority schedulers are widely used for real-time systems, and there were efforts to improve the schedulability. For example, preemption threshold scheduling increases the schedulability by introducing non-preemptiveness during task execution. In this paper, we study another scheduling model that can improve the schedulability of tasks: quantum-based scheduling. When quantum-based scheduling is combined with priority-based scheduling, the quantum introduces a partially non-preemptive region which causes a priority inversion. We show that the non-preemptiveness introduced by quantum-based scheduling can improve the schedulability of fixed priority tasks, and there is a task set which is not schedulable by preemption threshold scheduling but is schedulable by quantum-based scheduling. This paper also presents a method to calculate the worst case response time of tasks in quantum-based scheduling.

An Automated Test Code Generation Method for Web Applications using Activity Oriented Approach

Automated tests are important for Web applications as they grow more complex day by day. Web application testing frameworks have emerged to help satisfy this need. However, used without a model that is designed for system evolution and realization, maintaining test code becomes cumbersome and inefficient. This paper describes an activity oriented approach to engineer automated tests for Web applications with reference to a Web application developed for grant funding agencies. In this approach, the developer defines a domain model to represent application state, and uses a test activity graph comprised of self-validating user interactions to verify application correctness. We have implemented a test code generator called iTester using activity-oriented approach.

Distance Based Pre-cluster Head Selection Scheme for a Chain-Based Protocol

PEGASIS, a chain-based protocol, forms chains from sensor nodes so that each node transmits and receives from a neighbor. In this way, only one node (known as a HEAD) is selected from that chain to transmit to the sink. Although PEGASIS is able to balance the workload among all of the nodes by selecting the HEAD node in turn, a considerable amount of energy may be wasted when nodes which are far away from sink node act as the HEAD. In this study, DERP (Distance-based Energy-efficient Routing Protocol) is proposed to address this problem. DERP is a chain-based protocol that improves the greedy-algorithm in PEGASIS by taking into account the distance from the HEAD to the sink node. The main idea of DERP is to adopt a pre-HEAD (P-HD) to distribute the energy load evenly among sensor nodes. In addition, to scale DERP to a large network, it can be extended to a multi-hop clustering protocol by selecting a “relay node” according to the distance between the P-HD and SINK. Analysis and simulation studies of DERP show that it consumes up to 80% less energy, and has less of a transmission delay compared to PEGASIS.

Integration of Preemption Threshold and Quantum-Based Scheduling for Schedulability Enhancement of Fixed Priority Tasks

Fixed priority scheduling is an important real-time scheduling scheme widely used in practice. To improve the schedulability of fixed priority scheduling considerable effort has been made such as introduction of preemption threshold or deferred preemption, and quantum-based scheduling. In this paper, we develop a new scheduling scheme by introducing both preemption threshold and quantum into one scheduling framework. The new scheduling method may successfully schedule tasks which are not schedulable either by preemption threshold scheduling or quantum-based scheduling, as well as tasks schedulable by either scheduling, thus improves the schedulability of fixed-priority tasks. We analyze the schedulability of the new scheduler by computing the worst case response time of tasks. Based on the analysis, we developed an algorithm for assignment of preemption threshold and quantum sizes.

Neuron anatomy structure reconstruction based on a sliding filter

BackgroundReconstruction of neuron anatomy structure is a challenging and important task in neuroscience. However, few algorithms can automatically reconstruct the full structure well without manual assistance, making it essential to develop new methods for this task.MethodsThis paper introduces a new pipeline for reconstructing neuron anatomy structure from 3-D microscopy image stacks. This pipeline is initialized with a set of seeds that were detected by our proposed Sliding Volume Filter (SVF), given a non-circular cross-section of a neuron cell. Then, an improved open curve snake model combined with a SVF external force is applied to trace the full skeleton of the neuron cell. A radius estimation method based on a 2D sliding band filter is developed to fit the real edge of the cross-section of the neuron cell. Finally, a surface reconstruction method based on non-parallel curve networks is used to generate the neuron cell surface to finish this pipeline.ResultsThe proposed pipeline has been evaluated using publicly available datasets. The results show that the proposed method achieves promising results in some datasets from the DIgital reconstruction of Axonal and DEndritic Morphology (DIADEM) challenge and new BigNeuron project.ConclusionThe new pipeline works well in neuron tracing and reconstruction. It can achieve higher efficiency, stability and robustness in neuron skeleton tracing. Furthermore, the proposed radius estimation method and applied surface reconstruction method can obtain more accurate neuron anatomy structures.

A Pipeline for Neuron Reconstruction Based on Spatial Sliding Volume Filter Seeding

Neurons shape and dendritic architecture are important for biosignal transduction in neuron networks. And the anatomy architecture reconstruction of neuron cell is one of the foremost challenges and important issues in neuroscience. Accurate reconstruction results can facilitate the subsequent neuron system simulation. With the development of confocal microscopy technology, researchers can scan neurons at submicron resolution for experiments. These make the reconstruction of complex dendritic trees become more feasible; however, it is still a tedious, time consuming, and labor intensity task. For decades, computer aided methods have been playing an important role in this task, but none of the prevalent algorithms can reconstruct full anatomy structure automatically. All of these make it essential for developing new method for reconstruction. This paper proposes a pipeline with a novel seeding method for reconstructing neuron structures from 3D microscopy images stacks. The pipeline is initialized with a set of seeds detected by sliding volume filter (SVF), and then the open curve snake is applied to the detected seeds for reconstructing the full structure of neuron cells. The experimental results demonstrate that the proposed pipeline exhibits excellent performance in terms of accuracy compared with traditional method, which is clearly a benefit for 3D neuron detection and reconstruction.

A Hybrid Message Delivery Scheme for Improving Service Discovery in Mobile Ad-Hoc Networks

As mobile computing becomes more popular and prevalent, service discovery to search for a service available in the mobile ad-hoc networks (MANETs) is an important issue. Although mobile computing technologies grow to be ever more powerful and accessible, MANETs- consisting of mobile devices without any fixed infrastructures- possess inherent characteristics such as high mobility and resource constraints. Given these constraints, service discovery in the MANETs should be cost effective than that of the conventional networks in a fixed structure. In this paper, we design and evaluate the service discovery schemes to effectively discover services by using only local information in the MANETs. We first propose two schemes: lower ID scheme and higher degree scheme. Then, we propose a hybrid scheme which provides improved performance by utilizing the advantages, but avoiding the disadvantages of both the schemes. The simulation results show that the average lookup distance and the number of exchanged messages have been significantly reduced compared to the conventional schemes for service discovery.

Analysis on quantum-based fixed priority scheduling of real-time tasks

Fixed priority schedulers are widely used for real-time systems, and there were efforts to improve the schedulability. Preemption threshold scheduling is one of such efforts with a dual priority scheme. It increases the schedulability by introducing priority inversion during task execution. In this paper, we analyze another scheduling model, quantum-based scheduling. When quantum-based scheduling is combined with priority-based scheduling, the quantum introduces a partially non-preemptive region. We show that the non-preemptiveness introduced by quantum-based scheduling can improve the schedulability of fixed priority tasks, and there is a task set which is not schedulable by preemption threshold scheduling but is schedulable by quantum-based scheduling. This paper presents a method to calculate the worst case response time of tasks in quantum-based scheduling. Based on the previous research results for preemptive and non-preemptive scheduling, we present a priority assignment method for quantum-based scheduling also.