Chae-Wook Lee

Photon-counting passive 3D image sensing for reconstruction and recognition of partially occluded objects

In this paper, we discuss the reconstruction and the recognition of partially occluded objects using photon counting integral imaging (II). Irradiance scenes are numerically reconstructed for the reference target in three-dimensional (3D) space. Photon counting scenes are estimated for unknown input objects using maximum likelihood estimation (MLE) of Poisson parameter. We propose nonlinear matched filtering in 3D space to recognize partially occluded targets. The recognition performance is substantially improved from the nonlinear matched filtering of elemental images without 3D reconstruction. The discrimination capability is analyzed in terms of Fisher ratio (FR) and receiver operating characteristic (ROC) curves.

The Performance of an adaptive noise canceller with DSP processor

Among all adaptive noise cancellers, Widrow and Hoffs least mean square (LMS) algorithm has probably become the most popular because of its robustness, good tacking properties and simplicity of implementation. An important limitation of LMS algorithm is that the selection of a certain value for the step size implies compromise between speed of convergence and steady-state misadjustment, The variable step size normalized least-mean-square (VSS- NLMS) algorithm is appropriate to solve the conflicting requirement of fast convergence and low misadjustment of the LMS algorithm. To reduce noise in the output signal of the ANC, a step size for coefficient update is controlled according to the signal-to-noise ratio (SNR). This paper investigates the performance of a noise canceller with DSP processor (TI TMS320C6713) using the LMS algorithm, normalized least-mean-square (NLMS) algorithm, and VSS-NLMS algorithm. Results show the proposed combination of hardware and VSS-NLMS algorithm has not only a faster convergence rate but also lower distortion when compared with the fixed step size LMS algorithm and NLMS algorithm in real-time environments.

Hardware implementation of an Adaptive Noise canceller in an automobile environment

In this paper, we have researched the performance of an algorithm for an adaptive noise canceller (ANC) using the instantaneous signal to noise ratio variable step size algorithm (I-SNRVSS) in a real-time automobile environment. As a basic algorithm for ANC, the Least Mean Square (LMS) algorithm has been used for its simplicity. However, the LMS algorithm has problems of both convergence speed and estimation accuracy in a real-time environment. In order to solve these problems, many variable step size algorithms for ANC were applied in a nonstationary environment. This paper measures and investigates the performance of an adaptive noise canceller with a DSP processor (TI TMS320C6713) using the LMS algorithm and I-SNRVSS algorithm in an automobile environment. The I-SNRVSS algorithm turns out to be more effective than the LMS algorithm in both convergence speed and estimation accuracy for an ANC, especially for a colored input signal such as engine noise.

Performance analysis of adaptive noise canceller in real-time automobile environments

In this paper, we proposed an algorithm for adaptive noise cancellation (ANC) using the variable step size least mean square (VSSLMS) in real-time automobile environment. As a basic algorithm for ANC, the LMS algorithm has been used for its simplicity. However, the LMS algorithm has problems of both convergence speed and estimation accuracy in real-time environment. In order to solve these problems, the VSSLMS algorithm for ANC is considered in a nonstationary environment. By computer simulation using real-time data acquisition system (USB 6009), the VSSLMS algorithm turns out to be more effective than the LMS algorithm in both convergence speed and estimation accuracy, especially for the colored input signal used in ANC of engine noise.

Labview FPGA Implementation of IGC Algorithm for Real Time Noise Cancelation

The LMS(Least Mean Square) algorithm is generally used because of tenacity, high mating spots and simplicity of realization. But the LMS algorithm has trade-off between nonuniform collect and EMSE(Excess Mean Square Error). To overcome this weakness, variable step size is used widely but it needs a lot of calculation load. In this paper we consider new algorithm, which can reduce calculations and adapt in case of environment changes, uses original signal and noise signal of IGC(Instantaneous Gain Control). For the real time processing of IGC algorithm, we remove the logarithmic function. The performance of proposed algorithm is tested to adaptive noise canceller in automobile. We show implemented LabVIEW FPGA system of IGC algorithm is more efficient than others.

Implementation of the Embedded System for Visually-Impaired People

Since a 2-dimensional (2D) bar code can retrieve data and information quickly, it is widely used and recognized as a useful tool for many industrial applications. However, the information capacity of the 2D bar code is still limited. Recently the analog-digital code (AD code), which has the largest storage capacity yet contained in a code, has been developed, thereby expanding the bar codes application range because it overcomes the limitation of data capacity. In this paper, we present the AD code and an effective embedded system which can transform text information into voice using the 2D AD code and Text To Speech (TTS). This voice information can also be transmitted to visually-impaired people by capturing the AD code on paper or in books.

Comparison of adaptive systems for noise reduction in speech performance

An adaptive system is a well-known method for removing noise from noise-corrupted speech. The problem arises in many situations such as airplane cockpits and automobiles. The variable step size normalized least mean squares (VSS-NLMS) algorithm incorporating a variable step size parameter whose values are based on the ratio of signal-to-noise power has very good convergence speed and low steady-state misadjustment. This paper investigates the rate of convergence by the wavelet based LMS (WLMS) algorithm to the noise speech problem. Simulation results are presented to compare the performance of the variable step size NLMS algorithm, the wavelet based LMS Algorithm, and other commonly used fixed step size LMS algorithms