Erdal Oruklu

Bio: Erdal Oruklu is an academic researcher from Illinois Institute of Technology. The author has contributed to research in topics: Signal processing & Field-programmable gate array. The author has an hindex of 18, co-authored 135 publications receiving 1079 citations.

System-on-chip design for ultrasonic target detection using split-spectrum processing and neural networks

Abstract: Ultrasonic detection and characterization of targets concealed by scattering noise is remarkably challenging. In this study, a neural network (NN) coupled to split-spectrum processing (SSP) is examined for target echo visibility enhancement using experimental measurements with input signal-to-noise ratio around 0 dB. The SSP-NN target detection system is trainable and consequently is capable of improving the target-to-clutter ratio by an average of 40 dB. The proposed system is exceptionally robust and outperforms the conventional techniques such as minimum, median, average, geometric mean, and polarity threshold detectors. For realtime imaging applications, a field-programmable gate array (FPGA)-based hardware platform is designed for system-onchip (SoC) realization of the SSP-NN target detection system. This platform is a hardware/software co-design system using parallel and pipelined multiplications and additions for highspeed operation and high computational throughput.

Ultrasonic flaw detection using discrete wavelet transform for NDE applications

Abstract: In this work, we analyze signal decomposition properties of discrete wavelet transform (DWT) for enhanced ultrasonic flaw detection. In wavelet signal decomposition, a collection of time-frequency representations of the signal with different resolutions is obtained. DWT allows to utilize both time and frequency domain information for compacting and decorrelating the flaw echo from clutter echoes. In this paper, we present the performance analysis of different wavelet kernels with respect to ultrasonic NDE applications and develop the wavelet selection criteria for optimal flaw detection. Experimental results indicate that DWT based flaw detection algorithms offer flaw-to-clutter ratio enhancement of 5-12 dB when the measured flaw-to-clutter ratio is 0 dB or less. DWT flaw detection system can be implemented efficiently for real time applications using reconfigurable architecture and lifting scheme.

An Efficient FFT Engine With Reduced Addressing Logic

Abstract: In this study, an improved butterfly structure and an address generation method for fast Fourier transform (FFT) are presented. The proposed method uses reduced logic to generate the addresses, avoiding the parity check and barrel shifters commonly used in FFT implementations. A general methodology for radix-2 N-point transforms is derived and the signal flow graph for a 16-point FFT is presented. Furthermore, as a case study, a 16-point FFT with 32-bit complex numbers is synthesized using a CMOS 0.18 mum technology. The circuit gate count analysis indicates that significant logic reduction can be achieved with improved throughput compared to the conventional implementations.

3D image reconstruction and human body tracking using stereo vision and Kinect technology

Abstract: Kinect is a recent technology used for motion detection and human body tracking designed for a video game console. In this study, we explore two different types of 3D image reconstruction methods to achieve a new method for faster and higher quality 3D images. Generating depth perception information using high quality stereo image textures is computationally heavy and inefficient. On the other hand, depth information can be obtained very fast using Kinect but the overall 3D image quality is not refined and it is low resolution. Thus, in this study we explore the combination of higher quality images on a webcam and faster computation of depth information on Kinect in order to create an efficient and enhanced 3D image reconstruction system. This high resolution system has a broad range of applications including 3D motion sensing of human body, hands tracking and finger gestures.

FPGA-Based Traffic Sign Recognition for Advanced Driver Assistance Systems

Abstract: This paper presents the implementation of an embedded automotive system that detects and recognizes traffic signs within a video stream. In addition, it discusses the recent advances in driver assistance technologies and highlights the safety motivations for smart in-car embedded systems. An algorithm is presented that processes RGB image data, extracts relevant pixels, filters the image, labels prospective traffic signs and evaluates them against template traffic sign images. A reconfigurable hardware system is described which uses the Virtex-5 Xilinx FPGA and hardware/software co-design tools in order to create an embedded processor and the necessary hardware IP peripherals. The implementation is shown to have robust performance results, both in terms of timing and accuracy.

Detection, Estimation, and Modulation Theory

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A Survey of Applications and Human Motion Recognition with Microsoft Kinect

Abstract: 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.

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

Abstract: 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.