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

The Choi-Williams distribution (CWD) is an effective time-frequency (TF) distribution to suppress cross-terms. However, one notable drawback of the CWD is that it fails to suppress cross-terms when either center frequencies of two components are close or their time overlap is significant. In this paper, this problem is addressed by reconstructing TF distribution using basis functions which are extracted through Singular Value Decomposition (SVD) from CWD. The numerical analysis of multi-component Gaussian echoes are presented by using SVD of CWD. It is found that this decomposition and reconstruction approach efficiently eliminate residual cross-terms for which the CWD failed to remove. Results are presented in support of exhibiting the effectiveness of SVD of CWD for cross-term suppression.

Improved time-frequency distribution using singular value decomposition of Choi-Williams distribution

In this study, we address the increased computational demands of real-time ultrasonic data processing by developing an efficient frequency-diverse detection algorithm and architecture. Substantial computational savings can be obtained if the FIR filters utilized in frequency-diverse detection methods are replaced by IIR (infinite impulse response) filters. IIR filter implementations involve a much lower number of coefficients with similar filtering characteristics. However, the design of IIR filters results in phase distortion which shifts the frequency dependant signal features. This drawback greatly deteriorates the detection performance of the designed IIR bandpass filters. Consequently, for protection against phase distortion, an IIR zero-phase filter has been designed and presented. In this study, Reduced Adder Graph (RAG) algorithm is used for a hardware realization of IIR filters that does not require any dedicated multipliers. A small number of coefficients inherent to IIR filters and their multiplierless implementation provide an optimal architecture suitable for real-time ultrasonic imaging applications.

Efficient hardware realization of frequency-diverse ultrasonic flaw detection using zero-phase IIR filters

This paper describes a distributed sensor platform for a new breed of artificial pancreas devices. In recent work, a multi-variable adaptive algorithm has been proposed which incorporates physical activity of the patients for accurate prediction and control of glucose levels. In order to facilitate this algorithm, we integrate a smartphone and multiple sensors including activity trackers and a glucose monitor into a distributed system. The proposed sensor platform provides realtime data access for the artificial pancreas control algorithm hosted on a remote device.

Sensor fusion and distributed platform development for artificial pancreas

The detection and analysis of acoustic sources utilizing microphone arrays is a growing field of research. The evolving needs of applications such as noise suppression, voice recognition, sound tracking and acoustic source localization push requirements such as scalability and speed for sophisticated microphone array systems. Microphone arrays are advantageous compared to single microphone usage in that its use the physical spatial information of acoustic wave propagation to provide a more accurate picture of the incoming waves and therefore yield valuable information regard its source. This paper describes a broadband MEMS Array acouStic Imaging system called MASI that includes a highly scalable and network enabled FPGA based signal processing hardware for acquisition, processing and imaging. Design issues such as array sensitivity, accuracy and geometry of the array will be discussed as well as the system real-time capabilities regarding signal processing and data acquisition. Spatial localization and tracking are the main target applications for this system.

Scalable acoustic imaging platform using MEMS array

Driver Assistance Systems such as traffic sign detection and autonomous car research are largely facilitated with the recent advances on computer vision and pattern recognition. In this work, Bag of visual Words technique has been implemented on Speeded Up Robust Feature (SURF) descriptors of the traffic signs and later the sturdy classifier Support Vector Machine (SVM) is used to categorize the traffic signs to its respective groups. Experimental results demonstrate that the proposed method of implementation can reach an accuracy of 95.2%.

Erdal Oruklu

Papers

Improved time-frequency distribution using singular value decomposition of Choi-Williams distribution

Efficient hardware realization of frequency-diverse ultrasonic flaw detection using zero-phase IIR filters

Sensor fusion and distributed platform development for artificial pancreas

Scalable acoustic imaging platform using MEMS array

Traffic sign recognition based on prevailing bag of visual words representation on feature descriptors