Hyungshin Kim

Robust image watermarking using local invariant features

This paper addresses a novel robust watermarking method for digital images using local invariant features. Most previous water- marking algorithms are unable to resist geometric distortions that desyn- chronize the location where copyright information is inserted. We pro- pose a watermarking method that is robust to geometric distortions. In order to resist geometric distortions, we use a local invariant feature of the image called the scale-invariant feature transform SIFT, which is invariant to translation and scaling distortions. The watermark is inserted into the circular patches generated by the SIFT. Rotation invariance is achieved using the translation property of the polar-mapped circular patches. Our method belongs to the blind watermark, because we do not need the original image during detection. We have performed an inten- sive simulation to show the robustness of the proposed method. The simulation results support the contention that our method is robust against geometric distortion attacks as well as signal-processing attacks. We have compared our results with those of other methods, and our method outperforms them.

Smart phone power model generation using use pattern analysis

This paper presents an automated power model generation method for smart phone. Accurate power model can be achieved from usage pattern stored on the phone. This method can be applied to any mobile devices regardless of the manufacturer and model. The generated power model can be used to estimate energy consumption of the mobile device.

Automated power model generation method for smartphones

This paper presents an automated power model generation method for smartphones. Having an accurate power model of mobile devices can help identify the sources of fast discharge of the battery and provide opportunities for optimizing battery use. However, current methods rely on either external measurement equipment or internal current sensor support. This study presents a novel method for power modeling that exploits the usage patterns stored on the phone. This method can be applied to any mobile device regardless of the manufacturer and the model of the device. A power consumption analysis tool, and an artificial usage pattern generator tool have been developed for power modeling. The proposed method is able to generate an accurate power model of various smartphones even without detailed knowledge of the phone hardware.

Full virtualizing micro hypervisor for spacecraft flight computer

Recently partitioning and virtualization techniques for Integrated Modular Avionics (IMA) of aeronautics sector are proposed as the candidate architecture for safety-critical space applications. However, spacecraft software has subtle difference from aeronautic applications. Radiation particles in space environment cause various faults on the spacecraft computer. Requirement for autonomous operation with constrained resources is more stringent in space missions. Once it is launched, no way to refurbish the spacecraft without tremendous cost. These extra properties on top of those of regular aeronautic systems cause large software development cost in spacecraft projects. Summing up, spacecraft software should have real-time property, fault tolerance and efficient resource usage. In this paper, we introduce a hypervisor for spacecraft computer to improve reusability of inherited flight software from previous missions without redevelopment cycle. Fault tolerance is designed into the hypervisor to provide autonomous operation in space. We designed a prototype which is Type-II full virtualized hypervisor with kernel-level ARINC 653 partitioning on a dual-core LEON4-based flight computer for spacecraft. As the guest system, RTEMS-based flight software running on ERC32 flight computer is chosen because it has been used for many recent space missions and its flight software is likely to be reused when multicore LEON4 becomes widely available.

O-Sleep: Smartphones' Output-Oriented Power Saving Mode

Fast battery discharge is the most concerned issue for smart phone users. This is the result of the smart phones convergence trend and increasing complexity of its applications. Energy management techniques to reduce unnecessary battery usage are being studied. In this paper, we propose a new paradigm-shifting power saving mode for smart phones. O-Sleep is a smart phones output-oriented power saving mode. Rather than entering processor sleep mode when there is no user input, o-sleep puts a smart phones user interface into sleep mode when there is no meaningful output from the phone. While a device is processing a users request, output from the device may require processing time. We consider the situation as the devices output idle time and put the phones user interfaces into sleep mode while maintaining other subsystems in active state. To prove our concept, we have applied our technique onto various smart phone applications with varying operation environment. From the experiment, we have found we can save 37% of power consumption during up to 58% of its active usage with our test scenarios.

Synchronized operation by field programmable gate array based signal controller for the Thomson scattering diagnostic system in KSTAR

The Thomson scattering diagnostic system is successfully installed in the Korea Superconducting Tokamak Advanced Research (KSTAR) facility. We got the electron temperature and electron density data for the first time in 2011, 4th campaign using a field programmable gate array (FPGA) based signal control board. It operates as a signal generator, a detector, a controller, and a time measuring device. This board produces two configurable trigger pulses to operate Nd:YAG laser system and receives a laser beam detection signal from a photodiode detector. It allows a trigger pulse to be delivered to a time delay module to make a scattered signal measurement, measuring an asynchronous time value between the KSTAR timing board and the laser system injection signal. All functions are controlled by the embedded processor running on operating system within a single FPGA. It provides Ethernet communication interface and is configured with standard middleware to integrate with KSTAR. This controller has operated for two experimental campaigns including commissioning and performed the reconfiguration of logic designs to accommodate varying experimental situation without hardware rebuilding.

Rotation-Tolerant Watermark Detection Using Circular Harmonic Function Correlation Filter

Almost all of the previous watermarking methods utilize some form of correlation or matched filter during watermark detection. The conventional correlation detector provides optimal performance when the watermarked image goes through additive white noise. In this paper, we propose to use a circular harmonic function (CHF) correlation filter instead of the conventional matched filter at the detector. In this way, we can achieve rotation tolerant correlation peak at the detector. During the filter design, conflicting filter design parameters can be optimally traded off while achieving desired correlation peak in response to rotation of the image. The proposed detector can be used with any watermarking method where rotation of the watermark should be detected. As the filter design can be done off-line, the proposed method can be used in real-time watermark detectors.

Reducing distraction of smartwatch users with deep learning

Smartwatches are overloaded with various notifications from smartphones. Users are largely distracted, while they may benefit from these relayed notification. To reduce smartwatch users distraction, we propose an intelligent notification delivery system that relays only important notifications to the smartwatch. We claim that important notifications should be handled within a certain time and they are involved in launching mobile applications. To build model, we collect 6491 notifications and sensor data from three users. A mobile application has been developed to unobtrusively monitor relevant data Then, we implemented a binary classifier which identifies important notifications using deep learning and 8 features are extracted from sensor data. Our classifier shows that an important notification can be predicted with 61% - 90% and 51% - 99% of precision and recall.

Low Power Branch Predictor for Embedded Processors

Modern embedded processors highly rely on the accuracy of branch predictors. Large predictors with complex prediction algorithms cause more power consumption of the embedded systems which are already power hungry. Power consumed by a branch predictor can be reduced by removing unnecessary predictor accesses. In this paper we present the design of a low power branch predictor that exploits the well-behaved branches. Well-behaved branches are loops which show continuous Taken branch results. Our method is based on the Taken Branch Identification Table(TBIT). It maintains well-behaved taken-branch data that are updated by a 2-bit saturated counter learning algorithm. Through the TBIT, we eliminate unnecessary lookups and updates of a predictor when a well-behaved taken-branch instruction is executed repeatedly such as in a loop. The effectiveness of the proposed branch predictor is confirmed by the simulation results of the SPECint2000 and Mediabench benchmarks. The experimental results show that with negligible performance degradation, our method can reduce the power consumption of branch predictor about 40% and the processor total power consumption about 2.3% on average.

Output-oriented power saving mode for mobile devices

Abstract Fast battery discharge is still the most nerve wracking issue for smartphone users. Though many energy saving methods have been studied, still users are not satisfied with their phone’s battery. Power management system provides low power mode when the phone is not in use for a long time. When the user is interacting with the phone, current system assumes the user is interactive and should keep the device in active mode. However, this is not true. After the user’s interaction, the device processes the request and displays the result on the smartphone’s output device. During this period, the user cannot see any meaningful information from the phone. In this paper, we propose a new low power mode where we put smartphone’s output device into low power mode while phone is preparing result for the user. We named this as o-sleep, an output-oriented power saving mode. While a device is processing a user’s request, output from the device may require preparation time. We consider the situation as the device’s output idle time and put the phone’s user interfaces into sleep mode while maintaining other subsystems in active state. To prove our concept, we have applied our technique onto various smartphone applications with varying operation environment. From the experiment, we found that the smartphone entered the o-sleep mode up to 58% of its total usage time in various test scenarios. Usability study supported feasibility of our proposed method.