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

In this work, modeling of a stochastic non stationary process is used to detect flaw echoes in ultrasonic NDE signals. This research aims at locating the flaw (if any) present in the NDE signal using Hidden Markov Model (HMM). HMM works on the principle that any non-stationary stochastic signals are generated by a hidden process which can be approximated by a Markov Model. The states present in the Hidden Markov Model are unknown and may vary according to the application. For ultrasonic flaw detection application, three states were assumed to model low level noise, high level noise and the flaw. Discrete Wavelet Transform (DWT) was chosen as feature input to HMM. Experimental results show that flaws can be detected even in signals with very low signal to noise ratio. Compared to other regression methods such as Support Vector Regression, HMM performs better and requires less effort and A-scan data for training.

Ultrasonic flaw detection using Hidden Markov Model with wavelet features

This paper presents a management process for creating adaptive, real-time security policies within the Six Sigma (6σ) framework. A key challenge for the creation of a management process is the integration with models of known Industrial processes. One of the most used industrial process models is Six Sigma which is a business management model wherein customer centric needs are put in perspective with business data to create an efficient system. The security policy creation and management process proposed in this paper is based on the Six Sigma model and presents a method to adapt security goals and risk management of a computing service. By formalizing a security policy management process within an industrial process model, the adaptability of this model to existing industrial tools is seamless and offers a clear risk based policy decision framework. In particular, this paper presents the necessary tools and procedures to map Six Sigma DMAIC (Define-Measure-Analyze-Improve-Control) methodology to security policy management.

/pdf/security-policy-management-process-within-six-sigma-4mmagrrwr3.pdf

Security Policy Management Process within Six Sigma Framework

Ultrasonic 3D imaging is an important tool in NDE applications for quality control, flaw detection, and material characterization. However, ultrasonic 3D images often encompass immense amounts of data, making it very challenging for volumetric image analysis, transmission and storage. In this study, a fast and scalable data compression System-on-Chip (SoC) architecture based on Discrete Wavelet Transform (DWT) is proposed. This compression SoC can process A-Scan, B-Scan and C-Scan signals and images in real-time and reduce the data and bandwidth requirements substantially without degrading the signal fidelity. A volumetric image of 128x128x2048 samples is compressed by 96.9% in less than one second by the proposed compression system implemented on a Virtex-5 FPGA.

Adaptive 3D ultrasonic data compression using distributed processing engines

A flexible and efficient fixed to floating point conversion tool is presented for digital signal processing and communication systems. Fixed point numbers are heavily used in digital systems because they require less hardware, verification time and design effort compared to floating point number systems. However, floating point numbers offer better precision. Some digital designs may use a hybrid number system wherein fixed and floating point numbers can be used together to improve accuracy. The proposed design tool converts fixed-point numbers to floating-point numbers, including IEEE-754 floating point number standard. This tool generates Verilog RTL code and its testbench that can be implemented in FPGA and VLSI systems. The proposed design tool can increase productivity by reducing the design and verification time. The generated design has been implemented on Xilinx Virtex-5 FPGAs and compared to conventional fixed to floating conversion tools.

A high-level synthesis and verification tool for fixed to floating point conversion

Supervised Machine Learning (ML) algorithms such as Neural Networks, Support Vector Machines and Logistic Regression have been successfully utilized in Ultrasonic Non-Destructive Evaluation (NDE) applications. In supervised learning algorithms, data outputs are labeled and classified for training. In contrast, Unsupervised Machine Learning (UML) algorithms identify and exploit the commonalities in the data and no "ground truth" is necessary. In this work, we use three different UML algorithms based on K-means clustering, Gaussian Mixture Modeling and Mean Shift Clustering in order to detect and locate flaw echoes in ultrasonic A-Scan data. All three algorithms have been shown to perform flaw classification successfully. In particular, Gaussian Mixture Modeling achieves highest detection accuracy at 93%.

Erdal Oruklu

Papers

Ultrasonic flaw detection using Hidden Markov Model with wavelet features

Security Policy Management Process within Six Sigma Framework

Adaptive 3D ultrasonic data compression using distributed processing engines

A high-level synthesis and verification tool for fixed to floating point conversion

Unsupervised Machine Learning for Ultrasonic Flaw Detection using Gaussian Mixture Modeling, K-Means Clustering and Mean Shift Clustering