Numerical Methods for Scientists and Engineers

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Detection, Estimation, and Modulation Theory

Microsoft Kinect, a low-cost motion sensing device, enables users to interact with computers or game consoles naturally through gestures and spoken commands without any other peripheral equipment. As such, it has commanded intense interests in research and development on the Kinect technology. In this paper, we present, a comprehensive survey on Kinect applications, and the latest research and development on motion recognition using data captured by the Kinect sensor. On the applications front, we review the applications of the Kinect technology in a variety of areas, including healthcare, education and performing arts, robotics, sign language recognition, retail services, workplace safety training, as well as 3D reconstructions. On the technology front, we provide an overview of the main features of both versions of the Kinect sensor together with the depth sensing technologies used, and review literatures on human motion recognition techniques used in Kinect applications. We provide a classification of motion recognition techniques to highlight the different approaches used in human motion recognition. Furthermore, we compile a list of publicly available Kinect datasets. These datasets are valuable resources for researchers to investigate better methods for human motion recognition and lower-level computer vision tasks such as segmentation, object detection and human pose estimation.

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A Survey of Applications and Human Motion Recognition with Microsoft Kinect

IEEE transactions on very large scale integration (VLSI) systems

This paper presents a novel convolutional neural network (CNN) -based method for high-accuracy real-time car license plate detection. Many contemporary methods for car license plate detection are reasonably effective under the specific conditions or strong assumptions only. However, they exhibit poor performance when the assessed car license plate images have a degree of rotation, as a result of manual capture by traffic police or deviation of the camera. Therefore, we propose the a CNN-based MD-YOLO framework for multi-directional car license plate detection. Using accurate rotation angle prediction and a fast intersection-over-union evaluation strategy, our proposed method can elegantly manage rotational problems in real-time scenarios. A series of experiments have been carried out to establish that the proposed method outperforms over other existing state-of-the-art methods in terms of better accuracy and lower computational cost.

A New CNN-Based Method for Multi-Directional Car License Plate Detection

We introduce a systematic design flow consisting of two main stages for ultrasonic signal compression The first stage of the algorithm is concerned with finding the maximum energy compaction This is accomplished by finding an optimal subband decomposition tree structure for a particular combination of a wavelet kernel and the experimental input data The second stage of the algorithm is concerned with the coefficient reduction using thresholding techniques In addition to global thresholding, an adaptive thresholding is applied locally to each subband The iterative nature of the algorithm ensures that the scales (subbands) that retain most of the total signal energy are preserved while the coefficients in other scales (mostly higher detail scales) are eradicated aggressively The performance of the compression algorithm has been quantified with experimental data and shown to offer significant resilience to different experimental data sets

Ultrasonic signal compression using wavelet packet decomposition and adaptive thresholding

Recently, there has been a growing attention on fractional Fourier transform (FrFT) for time-frequency analysis In this investigation, an FrFT-based signal decomposition algorithm is utilized to decompose ultrasonic signals into a linear combination of signal components As a transformation tool, FrFT is employed to estimate an optimal transform order, which leads to the maximum amplitude response, or the highest kurtosis value in the fractional transform domain Furthermore, a signal component is obtained by applying a window in the fractional domain and inverse FrFT In an iterative manner, ultrasonic signals are decomposed into the signal components until a predefined stop criterion is satisfied Analytical and simulation results show that FrFT is an alternative method to perform high-resolution analysis of nonstationary ultrasonic signals

Performance evaluation of fractional Fourier transform(FrFT) for time-frequency analysis of ultrasonic signals in NDE applications

http://www.ece.iit.edu/~eoruklu/IIT/Publications_files/1-s2.0-S0167926010000192-main.pdf

Constant addition with flagged binary adder architectures

In this study, we address the increased computational demands of a frequency-diverse ultrasonic target detection system by developing a zero-phase IIR (ZP-IIR) filter. Several ZP-IIR filter types including Chebyshev-I, Chebyshev-II, and Butterworth were analyzed for their detection performance. The 4th-order filters with 8-bit quantized coefficients are shown to improve the flaw-to-clutter ratio by approximately 10 dB. Furthermore, the reduced adder graph algorithm is used for a hardware realization of ZP-IIR filters that does not require any dedicated multipliers. A small number of coef f icients inherent to IIR filters and their multiplierless implementation provide efficient architecture suitable for compact, real-time ultrasonic imaging devices.

Hardware-Efficient Realization of a Real-Time Ultrasonic Target Detection System Using IIR Filters

This paper presents a new computer vision design flow for real time detection and recognition of traffic signs. Autonomous traffic sign detection can play a crucial role in many applications related to transportation safety and geographical information systems. The challenges that need to be addressed include the necessity for robust and accurate detection as well as the high computational requirements of the algorithms. Therefore, we develop a three stage algorithm that is based on i) detection of traffic sign locations using HSV color space, ii) detection of traffic signs using discriminative features and iii) recognition of traffic signs using interest point descriptors. The results show a robust detection and recognition performance for multiple signs and the algorithm can be executed in real-time.

Erdal Oruklu

Papers

Ultrasonic signal compression using wavelet packet decomposition and adaptive thresholding

Performance evaluation of fractional Fourier transform(FrFT) for time-frequency analysis of ultrasonic signals in NDE applications

Constant addition with flagged binary adder architectures

Hardware-Efficient Realization of a Real-Time Ultrasonic Target Detection System Using IIR Filters

Real-time traffic sign detection and recognition for in-car driver assistance systems