Ki-Hyeon Kwon

Blind signal processing for impulsive noise channels

In this paper, a new blind signal processing scheme for equalization in fading and impulsive-noise channel environments is introduced based on probability density function matching method and a set of Dirac-delta functions. Gaussian kernel of the proposed blind algorithm has the effect of cutting out the outliers on the difference between the desired level values and impulse-infected outputs. And also the proposed algorithm has relatively less sensitivity to channel eigenvalue ratio and has reduced computational complexity compared to the recently introduced correntropy algorithm. According to these characteristics, simulation results show that the proposed blind algorithm produces superior performance in multi-path communication channels corrupted with impulsive noise.

On Deployment of Multiple Base Stations for Energy-Efficient Communication in Wireless Sensor Networks

Data transmission from sensor nodes to a base station or a sink node often incurs significant energy consumption, which critically affects network lifetime. We generalize and solve the problem of deploying multiple base stations to maximize network lifetime in terms of two different metrics under one-hop and multihop communication models. In the one-hop communication model, the sensors far away from base stations always deplete their energy much faster than others. We propose an optimal solution and a heuristic approach based on the minimal enclosing circle algorithm to deploy a base station at the geometric center of each cluster. In the multihop communication model, both base station location and data routing mechanism need to be considered in maximizing network lifetime. We propose an iterative algorithm based on rigorous mathematical derivations and use linear programming to compute the optimal routing paths for data transmission. Simulation results show the distinguished performance of the proposed deployment algorithms in maximizing network lifetime.

A Lightweight Lap Time Measurement System for Alpine Ski Sport Using a TDMA-Based Linear Wireless Sensor Network

In Alpine ski sport, traditional lap time measurement systems record a skiers departure and arrival using time-of-day timers through wires, which is not cost effective in unofficial or training games. This paper develops a lightweight lap time measurement system using a wireless sensor network, which employs a practical TDMA-based linear wireless sensor network protocol for multihop communications in long strap-shaped environments where sensor nodes are linearly deployed. We evaluate the performance of this protocol through the implementation and deployment of the lap time measurement system. The experimental results show that the proposed protocol transmits data at a success rate of 99.66% and maintains the time synchronization errors between two adjacent nodes within 183 μs without using any other time synchronization protocols. In an experimental deployment for a 1.5 km ski slope covered by 31 nodes, the system provides lap time measurements with a measurement error of 1.255 ms, which satisfies the official measurement error of 5.0 ms.

Stabilizing transport dynamics of control channels over wide-area networks

The next generation large-scale computing applications require network support for interactive visualization, computational steering and instrument control over wide-area networks. In particular, these applications require stable transport streams over wide-area networks, which are not adequately supported by current transport methods. We propose a new class of protocols capable of stabilizing a transport channel at a specified throughput level in the presence of random network dynamics based on the classical Robbins-Monro stochastic approximation approach. These protocols dynamically adjust the window size or sleep time at the source to achieve stable throughput at the destination. The target throughput typically corresponds to a small fraction of the available connection bandwidth. This approach yields provably probabilistically stable protocols as a consequence of carefully adjusted step sizes. The superior and robust stabilization performance of the proposed approach is extensively evaluated in a simulated environment and further verified through real-life implementations and deployments over both Internet and dedicated connections under disparate network conditions in comparison with existing transport methods.

Lagged cross-correlation of probability density functions and application to blind equalization

In this paper, the lagged cross-correlation of two probability density functions constructed by kernel density estimation is proposed, and by maximizing the proposed function, adaptive filtering algorithms for supervised and unsupervised training are also introduced. From the results of simulation for blind equalization applications in multipath channels with impulsive and slowly varying direct current (DC) bias noise, it is observed that Gaussian kernel of the proposed algorithm cuts out the large errors due to impulsive noise, and the output affected by the DC bias noise can be effectively controlled by the lag τ intrinsically embedded in the proposed function.

Normalized Minimum Error Entropy Algorithm with Recursive Power Estimation

The minimum error entropy (MEE) algorithm is known to be superior in signal processing applications under impulsive noise. In this paper, based on the analysis of behavior of the optimum weight and the properties of robustness against impulsive noise, a normalized version of the MEE algorithm is proposed. The step size of the MEE algorithm is normalized with the power of input entropy that is estimated recursively for reducing its computational complexity. The proposed algorithm yields lower minimum MSE (mean squared error) and faster convergence speed simultaneously than the original MEE algorithm does in the equalization simulation. On the condition of the same convergence speed, its performance enhancement in steady state MSE is above 3 dB.

Implementation of a DB-Based Virtual File System for Lightweight IoT Clouds

ABSTRACT IoT(Internet of Things) is a concept of connected internet pursuing direct access to devices or sensors in fused environment of personal, industrial and public area. In IoT environment, it is possible to access realtime data, and the data format and topology of devices are diverse. Also, there are bidirectional communications between users and devices to control actuators in IoT. In this point, IoT is different from the conventional internet in which data are produced by human desktops and gathered in server systems by way of one-sided simple internet communications. For the cloud or portal service of IoT, there should be a file management framework supporting systematic naming service and unified data access interface encompassing the variety of IoT things. This paper implements a DB-based virtual file system maintaining attributes of IoT things in a UNIX-styled file system view. Users who logged in the virtual shell are able to explore IoT things by navigating the virtual file system, and able to access IoT things directly via UNIX-styled file I⋅O APIs. The implemented virtual file system is lightweight and flexible because it maintains only directory structure and descriptors for the distributed IoT things. The result of a test for the virtual shell primitives such as mkdir() or chdir() shows the smooth functionality of the virtual file system, Also, the exploring performance of the file system is better than that of Window file system in case of adopting a simple directory cache mechanism.Keywords:IoT(Internet of Things), Cloud Service, VFS(Virtual File System), DB-Based File System

Blind Equalization Using Correntropy Algorithm for Wireless Sensor Networks

—Wireless links in indoor sensor networks have distortions due to multipath fading from reflections and impulsive noise from indoor electric devices. In these harsh environments, blind correntropy equalization algorithm yields superior MSE performance compared with the constant modulus algorithm. However the correntropy algorithm has a heavy computational complexity, which is not suitable for power and cost effectiveness demanded in wireless sensor networks. In this paper, a new gradient estimation for weight updates of the correntropy algorithm in order to reduce its computational burden is proposed. For the size of the data block, N-M+1 including the number of lags M, the conventional correntropy algorithm requires (N+1)M  (M+1)M/2 multiplications, whereas the proposed method of recursively estimating the gradient does only M 2. The simulation results show that the conventional and proposed gradient estimation methods yield exactly the same estimation traces proving justification of the proposed estimation. These results indicate that the proposed method can be implemented in reliable and efficient indoor sensor networks.

Collision Avoidance for Indoor Mobile Robotics using Stereo Vision Sensor

We detect the obstacle for the UGV(unmanned ground vehicle) from the compound image which is generated by stereo vision sensor masking the depth image and color image. Stereo vision sensor can gathers the distance information by stereo camera. The obstacle information from the depth compound image can be send to mobile robot and the robot can localize the indoor area. And, we test the performance of the mobile robot in terms of distance between the obstacle and the robot`s position and also test the color, depth and compound image respectively. Moreover, we test the performance in terms of number of frame per second which is processed by operating machine. From the result, compound image shows the improved performance in distance and number of frames.

Decision feedback approach to blind algorithms in impulsive noise environments

In order to cope with impulsive noise and error propagation problems without any additional schemes, new decision feedback algorithms with inherent robustness against impulsive noise are introduced in this paper. The Gaussian kernel with a mathematically constructed information potential makes the algorithms insensitive to large differences between symbol points and outputs induced by impulsive noise. This property effectively prevents decision feedback strategies from incorrect decisions and error propagation. From the simulation results, the proposed strategy has yielded significant performance enhancement of 7 dB over the previously proposed scheme.