Sung Y. Shin

Secure deletion for NAND flash file system

In most file systems, if a file is deleted, only the metadata of the file is deleted or modified and the files data is still stored on the physical media. Some users require that deleted files no longer be accessible. This requirement is more important in embedded systems that employ flash memory as a storage medium. In this paper, we have designed a NAND flash file system that has a secure deletion functionality. We modified YAFFS to support secure deletion. Our method uses encryption to delete files and forces all keys of a specific file to be stored in the same block. Therefore, only one erase operation is required to securely delete a file. The proposed method securely deletes not only keys but also all of the metadata of that file. Our simulation results show that the number of block erases due to file creation and file modification is very low and the amortized number of block erases is lower than the simple encryption method. Even though we applied our method only to the YAFFS, our method can be easily applied to other NAND flash file systems.

Space-efficient page-level incremental checkpointing

Incremental checkpointing, which is intended to minimize checkpointing overhead, saves only the modified pages of a process. However, the cumulative size of incremental checkpoints increases at a steady rate over time because many updated values may be saved for the same page. In this paper, we present a comprehensive overview of Pickpt, which is a page-level incremental checkpointing facility. Pickpt provides space-efficient techniques for minimizing the use of disk space. For our experiments, the results show that the use of disk space of Pickpt was significantly reduced compared with existing incremental checkpointing.

Effective tumor feature extraction for smart phone based microwave tomography breast cancer screening

Mobile Microwave Tomography (MMT) is a new alternative technique to detect breast cancer using smart phone based electronic healthcare system. In this paper, we propose a new solution to extract tumor information from MMT raw data for early breast cancer screening. MMT reflects water contents of breast tissue by measuring their electrical properties and sends permittivity and conductivity raw data to processing servers in hospital via WiFi or 3G/4G networks. In this approach we investigate three different sets of MMT tumor features and perform a comparative study to investigate their set of accuracy measurements for each classification. Through extensive empirical study of the classification results, we have identified the following six parameters as useful to extract tumor information: average permittivity of healthy tissue (APHT), average permittivity of probable tumor area (APPTA), maximum and minimum values of permittivity of probable tumor area (MaxPPTA, and MinPPTA), and energy values of healthy tissue (EVHT) and probable tumor area (EVPA).

Evaluation of interval-based dynamic voltage scaling algorithms on mobile Linux system

During the last several years, dynamic voltage scaling (DVS) algorithms are being used for energy consumption on real, fully functional battery supplied devices, adjusting the clock speed and supply voltage dynamically. Most DVS algorithms are investigated in interval-based and task-based strategies. Task-based algorithms consider task information, especially task deadline, on deciding what speed to choose at any given time. Interval-based algorithms predict the CPU speed of the upcoming interval based on observations of the CPU utilization of previous intervals, and then set the speed for that interval based on this prediction. Most DVS algorithms have only been tested in simulation environments. In this paper, those interval-based DVS algorithms are modified with different parameters on different workloads, and evaluated to know which one saves the most energy while not degrading computer performance.

Design and implementation of a kernel resource protector for robustness of Linux module programming

Loadable kernel modules supported by Linux provides lots of benefits such as a small-sized kernel, on-demand loading, and easy software upgrading. However, since modules are executed in a privileged mode, trivial misuses in a module may cause critical system halts or deadlock situations. This paper presents a kernel resource protector which prevents kernel from faults generated by modules. The protector models the system in two objects: module object and resource object. By observing the interrelations between the two objects, the protector can detect misuses of modules and take actions to resolve the erroneous situations. Implementation study has shown that the protector can find out memory leaks wasted by modules and can reclaim leaks without degrading system performance. The proposed protector makes Linux more robust, which is required indispensably in the system equipped with NVRAM (Non Volatile RAM) such as FRAM and PRAM.

Fourier irregularity index: A new approach to measure tumor mass irregularity in breast mammogram images

Shape descriptors have been identified as important features in distinguishing malignant masses from benign masses. Thus, an effective morphological irregularity measure could provide a helpful reference to indicate the likelihood of malignancy of breast masses. In this paper, a new Fourier-Transform-based measure of irregularity—Fourier Irregularity Index (F2), is proposed to provide reliable malignant/benign tumor/mass classification. The proposed measure has been evaluated on 418 breast masses, including 190 malignant masses and 218 benign lesions identified by radiologists on film mammograms. The results show the proposed measure has better performance than other approaches, such as Compactness Index (CI), Fractal Dimension (FD) and the Fourier-descriptor-based shape Factor (FF). Furthermore, these mentioned measures are paired to investigate the possibility of performance improvement. The results showed the combination of F2 and CI further enhances the performance in indicating the likelihood of malignancy of breast masses.

Vessel Segmentation Model using Automated Threshold Algorithm from Lower Leg MRI

Blood vessel segmentation has been developed in the liver, heart, and retinal images due to accurate description and analysis of vascular structure plays a crucial role in clinical routine. Since the varicose vein, deep vein thrombosis, and occlusive arterial diseases are related to vascular structure in the lower leg, blood vessel segmentation in lower limbs is also clinically important. In this paper, we proposed a feature-based adaptive threshold model for automatically extracting vessel in the lower leg Magnetic Resonance Images (MRIs). The proposed model is divided into 2 stages. The first stage, pre-processing, included partial volume reduction, contrast equalization, and removing background noises. The second stage is segmentation stage. Fuzzy C-mean clustering, Hough transform in feature extraction technique, and threshold algorithm were included in the second stage. Automatic threshold value determination algorithm is enhanced by using the Hough transform in feature extraction technique. The proposed model has been implemented for showing accuracy (ACC) compared with a manually generated ground truth from domain experts. Results show that proposed model has the accuracy with the average 98.43%, which is higher than existing model, Adaptive Vein Segmentation (AVS) method as a reference [1].

Study of wireless mammography image transmission impacts on robust cyber-aided diagnosis systems

Wireless cyber-mammography is potentially a convenient screening method to be comfortable and effective in community and rural area early detection of breast cancer, but their interpretation is difficult due to the noise and low quality of images. In this paper, we study the accuracy of a Cyber-aided diagnosis system to help physicians to classify the detected regions in wireless mammogram images into malignant or benign categories. In this approach we investigate different sets of features and two classifier methods (SVM and GMM) and perform a comparative study to investigate the accuracy measurements in noisy condition. The results show that without any noise or errors, SVM classifier outperforms GMM; however GMM classifier is more robust and reliable in noisy circumstance especially in detecting malignant cases. The proposed study provides in-depth understanding of the accuracy and reliability of wireless mammography in early breast cancer detection.

Similar MRI object retrieval based on modified contour to centroid triangulation with arc difference rate

In this paper, we propose a new image retrieval method based on Sectored Contour to Centroid Triangulation (SCTCT) using distinctive shape feature, named Arc Difference Rate (ADR). We utilized Support Vector Machine (SVM) method as an extraction tool to extract suspicious tumor area as binary object image from the breast MRI. Therefore extracted 100 binary object images are used as test cases in the experimental study. The results from proposed method show the improvement in finding correct matches compare to the traditional SCTCT.

Fault tolerant framework and techniques for component-based autonomous robot systems

Due to the benefits of its reusability and productivity, the component-based approach has become the primary technology in service robot software frameworks, such as MRDS (Microsoft Robotics Developer Studio), RTC (Robot Technology Component), ROS (Robot Operating System) and OPRoS (Open Platform for Robotic Services). However, all the existing frameworks are very limited in fault tolerance support, even though the fault tolerance function is crucial for the commercial success of service robots. In this paper, we present a rule-based fault tolerant framework together with widely-used, representative fault tolerance measures. With our observation that most faults in components and applications in service robot systems have common patterns, we equip the framework with the required fault tolerant functions. The system integrators construct fault tolerance applications from non-fault-aware components by declaring fault handling rules in configuration descriptors or/and adding simple helper components, considering the constraints of the components and the operating environment. Much more consistency in system reliability can be obtained with less effort of system developer. Various fault scenarios with a test robot system on the proposed OPRoS fault tolerant framework demonstrate the benefits and effectiveness of the proposed approach.