DSpace collection: 期刊論文

Robust image watermarking based on compressed sensing techniques

title: Robust image watermarking based on compressed sensing techniques abstract: Compressed sensing is a newly developed topic in the field of data compression. Most of relating researches focus on compression performances or theoretical studies, and there are very few papers aiming at the integration of watermarking into compressed sensing systems. In this paper, we propose an innovative scheme that considers the copyright protection of data with compressed sensing. By carefully utilizing the relationships between compressively sensed coefficients, very few amounts of transmitted coefficients are capable of reconstructing the image to some extent. Moreover, secret information embedded beforehand can be recovered with acceptable rate in correctly extracted bits even experiencing through the lossy channels for delivery of marked image. Simulation results with our algorithm have demonstrated the effectiveness for integrating watermarking into compressive sampling systems.

Data Mining Based Intelligent System for Voting Behavior Analysis

title: Data Mining Based Intelligent System for Voting Behavior Analysis abstract: In this study, we report a voting behavior analysis intelligent system based on data mining technology. From previous literature, we have witnessed increasing number of studies applied information technology to facilitate voting behavior analysis. In this study, we built a likely voter identification model through the use of data mining technology, the classification algorithm used here constructs decision tree model to identify voters and non voters. This model is evaluated by its accuracy and number of attributes used to correctly identify likely voter. Our goal is to try to use just a small number of survey questions while maintaining the accuracy rates of other similar models. This model was built and tested on Taiwan’s Election and Democratization Study (TEDS) data sets. According to the experimental results, the proposed model can improve likely voter identification rate and this finding is consistent with previous studies based on American National Election Studies.

Multi-Level Reversible Data Hiding with Prediction-Based Approach

title: Multi-Level Reversible Data Hiding with Prediction-Based Approach abstract: In this paper, we propose a new algorithm in reversible data hiding with prediction-based scheme.Reversible data hiding can be implemented with two types, one is by modifYing the histogram of images, named the histogram-based scheme, and the other is by changing the difference value between neighboring pixels, called the difference-expansion-based (DE-based) method. Considering the ease of implementation, we employ the histogram-based scheme with the concepts from the DE-based methods in our algorithm. For hiding the secret information, the differences between original and predicted images are produced firstly, and they are intentionally altered to make reversible data hiding possible. By utilizing the advantages from the two types of methods, by change of histograms of difference values, global and local characteristics of original images can be utilized for hiding more capacity with acceptable quality of output image. With our method, it performs better in embedding capacity, image quality, and side information than conventional algorithm in literature. It also has the potential for the integration to relating algorithms for practical applications.

Hierarchy-based reversible data hiding

title: Hierarchy-based reversible data hiding abstract: In this paper, we propose a new method for reversible data hiding by employing the hierarchical relationships of original images. There are many parameters for accessing the performances of reversible data hiding algorithms, including the output image quality, the hiding capacity, and the overhead for decoding. Considering the ease of implementation and the little overhead needed for decoding, we employ modification of difference values between pixels by using histogram-based scheme with extensions to pyramidal structure by utilizing inherent characteristics of original images. By doing so, global and local characteristics of original images can be utilized for hiding more capacity with acceptable quality of output image. With our method, better performances can be obtained with enhanced image quality, the more embedding capacity, and comparable amount of side information for decoding. More importantly, the reversibility of our method is guaranteed, meaning that original image and hidden message can both be perfectly recovered at the decoder. Simulation results demonstrate that proposed method in this paper outperforms those in conventional algorithms.

A Real-Time Fire Evacuation System with Cloud Computing

title: A Real-Time Fire Evacuation System with Cloud Computing abstract: An effective evacuation system can help people escape from building fire. Most evacuation systems consist of an indoor positioning system, a back-end database, and a display device with calculation and display software. However, very few of them can smartly determine which evacuation route is the best decision. If all the locations of the evacuating people can be simultaneously determined, the best evacuation routes can be decided to avoid congestion, and survival rate can increase. The previous radio frequency identification (RFID) based evacuation system focused on detecting the RFID tags using a mobile phone in order to determine the location of the mobile phone user so that an evacuation route can be displayed. However, the system is available for one person regardless of the number of evacuating people or exits. This study is based on the previous RFID based evacuation system investigating the best evacuation routes. The system introduces cloud computing that calculates for positioning the evacuating people and determining the optimum evacuation routes for each of them.

A refactoring method for cache-efficient swarm intelligence algorithms

title: A refactoring method for cache-efficient swarm intelligence algorithms abstract: With advances in hardware technology, conventional approaches to software development are not effective for developing efficient algorithms for run-time environments. The problem comes from the overly simplified hardware abstraction model in the software development procedure. The mismatch between the hypothetical hardware model and real hardware design should be compensated for in designing an efficient algorithm. In this paper, we focus on two schemes: one is the memory hierarchy, and the other is the algorithm design. Both the cache properties and the cache-aware development are investigated. We then propose a few simple guidelines for revising a developed algorithm in order to increase the utilization of the cache. To verify the effectiveness of the guidelines proposed, optimization techniques, including particle swarm optimization (PSO) and the genetic algorithm (GA), are employed. Simulation results demonstrate that the guidelines are potentially helpful for revising various algorithms.

A fast adaptive power scheme based on temperature distribution and convergence value for optimal hyperthermia treatment

title: A fast adaptive power scheme based on temperature distribution and convergence value for optimal hyperthermia treatment abstract: To elevate tissue temperature to therapeutic level fast with optimal power deposition during hyperthermia treatment (HT) is a key treatment processing step. Traditionally we have treated the tumor volume, without considering possible existing thermally significant vessels, using a simple 1st-order temperature-based adaptive power scheme to determine optimal power deposition distributions. The objectives of this study were to reveal the difficulty of that approach when considering single large blood vessel, and proposed a novel fast scheme that could improve upon and substitute for the traditional temperature-based adaptive power scheme. In this study, we presented a novel three-coefficient-andtwo-SCV 5th-order temperature-based adaptive power scheme to resolve the induced large blood vessel problem in 3-D temperature distribution and introduced the parameter, SCV (Sentinel Convergence Value), to handle interior scheme shift. Results of the novel adaptive power scheme has shown its robustness to fast approach optimal temperature distribution and power density distribution with high precision in the tumor volume when considering the existence of thermally significant blood vessel. Ultimately, we may be able to effectively calculate the absorbed power density distribution of 3-D biological tissues with a complicated vasculature in the volume.

Reversible data hiding with histogram-based difference expansion for QR code applications

title: Reversible data hiding with histogram-based difference expansion for QR code applications abstract: In this paper, we propose a new algorithm in reversible data hiding, with the application associated with the quick response (QR) codes. QR codes are random patterns, which can be commonly observed on the corner of posters or webpages. The goal of QR codes aims at convenienceoriented applications for mobile phone users. People can use the mobile phone cameras to capture QR code at the corner of web page, and then the hyperlink corresponding to the QR code can be accessed instantly. Since QR code looks like random noise and it occupies a corner of the original image, its existence can greatly reduce the value of the original content. Thus, how to retain the value of original image, while keeping the capability for the instant access for webpages, would be the major concern of this paper. With the aid of our reversible data hiding technique, the QR codes can be hidden into the original image, and considerable increase in embedding capacity can be expected. Next, we propose a scheme such that when the image containing the QR code is browsed, the hyperlink corresponding to the QR code is accessed first. Then, the QR code could get vanished and the original image would be recovered to retain the information conveyed therein. Simulation results demonstrate the applicability of the proposed algorithm.
description: 100學年度研究獎補助論文

IEEE 802.11 DCF 在隱藏節點影響下的性能分析

title: IEEE 802.11 DCF 在隱藏節點影響下的性能分析

Computer Simulation of 3-D Temperature and Power Distributions in Tissue with a Countercurrent Blood Vessels Network during Hyperthermia

title: Computer Simulation of 3-D Temperature and Power Distributions in Tissue with a Countercurrent Blood Vessels Network during Hyperthermia abstract: A countercurrent blood vessel network (CBVN) model for calculating tissue temperatures has been developed for studying optimized hyperthermia cancer treatment. This type of model represents a more fundamental approach to modeling temperatures in tissue than do the generally used approximate equations such as the Pennes' bio-heat transfer equation (BHTE) or effective thermal conductivity equations. The 3-D temperature distributions are obtained by solving the conduction equation in the tissue and the convective energy equation with specified Nusselt number in the vessels. This study uses an optimization scheme to investigate the impact of thermally significant blood vessels during hyperthermia cancer treatment. The optimization scheme used here is adjusting power based on the local temperature in the treated region in an attempt to reach the ideal therapeutic temperature of 43℃. The scheme can be used (or adapted) in a non-invasive power supply application such as high-intensity focused ultrasound (HIFU). Results show that first, a large amount of thermal absorbed power is focused on the locations near (or in) blood vessels and/or dense vessels in the treated tumor region during hyperthermia treatment as mass flow rates contribute cooling effects from vessels to tissues. Second, veins also play a significant role of affecting temperature distributions in the treated region and need to be taken into consideration.
description: 99學年度黃煌文教師升等參考著作

Predicting effects of blood flow rate and size of vessels in a vasculature on hyperthermia treatments using computer simulation

title: Predicting effects of blood flow rate and size of vessels in a vasculature on hyperthermia treatments using computer simulation abstract: BACKGROUND: Pennes Bio Heat Transfer Equation (PBHTE) has been widely used to approximate the overall temperature distribution in tissue using a perfusion parameter term in the equation during hyperthermia treatment. In the similar modeling, effective thermal conductivity (Keff) model uses thermal conductivity as a parameter to predict temperatures. However the equations do not describe the thermal contribution of blood vessels. A countercurrent vascular network model which represents a more fundamental approach to modeling temperatures in tissue than do the generally used approximate equations such as the Pennes BHTE or effective thermal conductivity equations was presented in 1996. This type of model is capable of calculating the blood temperature in vessels and describing a vasculature in the tissue regions. METHODS: In this paper, a countercurrent blood vessel network (CBVN) model for calculating tissue temperatures has been developed for studying hyperthermia cancer treatment. We use a systematic approach to reveal the impact of a vasculature of blood vessels against a single vessel which most studies have presented. A vasculature illustrates branching vessels at the periphery of the tumor volume. The general trends present in this vascular model are similar to those shown for physiological systems in Green and Whitmore. The 3-D temperature distributions are obtained by solving the conduction equation in the tissue and the convective energy equation with specified Nusselt number in the vessels. RESULTS: This paper investigates effects of size of blood vessels in the CBVN model on total absorbed power in the treated region and blood flow rates (or perfusion rate) in the CBVN on temperature distributions during hyperthermia cancer treatment. Also, the same optimized power distribution during hyperthermia treatment is used to illustrate the differences between PBHTE and CBVN models. Keff (effective thermal conductivity model) delivers the same difference as compared to the CBVN model. The optimization used here is adjusting power based on the local temperature in the treated region in an attempt to reach the ideal therapeutic temperature of 43 degrees C. The scheme can be used (or adapted) in a non-invasive power supply application such as high-intensity focused ultrasound (HIFU). Results show that, for low perfusion rates in CBVN model vessels, impacts on tissue temperature becomes insignificant. Uniform temperature in the treated region is obtained. CONCLUSION: Therefore, any method that could decrease or prevent blood flow rates into the tumorous region is recommended as a pre-process to hyperthermia cancer treatment. Second, the size of vessels in vasculatures does not significantly affect on total power consumption during hyperthermia therapy when the total blood flow rate is constant. It is about 0.8% decreasing in total optimized absorbed power in the heated region as gamma (the ratio of diameters of successive vessel generations) increases from 0.6 to 0.7, or from 0.7 to 0.8, or from 0.8 to 0.9. Last, in hyperthermia treatments, when the heated region consists of thermally significant vessels, much of absorbed power is required to heat the region and (provided that finer spatial power deposition exists) to heat vessels which could lead to higher blood temperatures than tissue temperatures when modeled them using PBHTE.
description: 99學年度黃煌文教師升等代表著作