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 study, we present a low-cost, low-power, and compact hardware platform for real-time ultrasonic detection applications In order to cope with the computational complexity of the inherent signal processing algorithms, recursive IIR filter architectures are proposed for realizing the frequency diverse transforms; discrete Fourier transform (DFT) and discrete cosine transform (DCT), which provide robust detection performance Both Goertzel (for DFT implementation) and folding kernel for DCT offer significant hardware reductions compared to standard transform implementations In order to sustain the high throughput required for real-time signal processing, a sparse transform method is proposed where only the required transform coefficients are computed FPGA implementation results confirm savings in hardware usage and viable performance for a portable ultrasonic imaging device

Recursive filters for subband decomposition algorithms in ultrasonic detection applications

Matrix operations are required in many complex algorithms in digital, image and video processing applications. The conventional method is usually used to implement matrix multiplications for small matrices. However, with the development of VLSI technology and FPGAs, there is an increasing demand for developing a high speed, low power and low area matrix multiplication system for large matrices. The design and verification process of an area-efficient and high throughput matrix multiplication operator can be time consuming and complex. In this study, an efficient hardware design tool is developed to generate a matrix multiplication operator based on user input parameters using three different approaches (Conventional, Strassen, and Hybrid). Strassen algorithm can be used to reduce the area of the matrix multiplication system; nevertheless this method increases the memory requirements and decreases the accuracy of the results. The proposed Hybrid-Strassen matrix multiplication algorithm increases the precision of the operation while reducing the area of the overall system compared to a conventional approach. The generated design has been implemented on Xilinx Virtex-5 FPGAs but can be synthesized for VLSI implementations.

An efficient hardware design tool for scalable matrix multiplication

Classification of Ultrasonic Non-Destructive Testing (NDT) signals can be done by Machine Learning models including Support Vector Machine (SVM) and Neural Networks (NN). The main objective of this study is to classify the ultrasonic A-scan data either as flaw echoes or clutter echoes (no flaw). The signal pre-processing has been done using Discrete Wavelet Transform (DWT). The refined low pass output has been used as feature input to the machine learning algorithms either directly or as a power signal. In case of SVM, direct low pass output in windowed format was tested with linear kernel and Radial Basis Function (RBF) kernel and the power signal of the low pass DWT in the windowed format was also tested with linear kernel and RBF kernel. SVM simulation results show that the direct low pass signal with linear kernel fails to converge while power of the low pass DWT achieves an accuracy of around 95%. RBF kernel accuracy was around 98% irrespective of the format of the signal. In case of Neural Network, both the direct low pass output and the power of the low pass output were tested and it was found that the direct low pass output with NN yielded 94% accuracy while the power of the low pass output with NN yielded an accuracy of 98%.

Investigation of Feature Inputs for Binary Classification of Ultrasonic NDT Signals using SVM and Neural Networks

Decimal arithmetic is desirable for high precision requirements of many financial, industrial and scientific applications. Furthermore, hardware support for decimal arithmetic has gained momentum with IEEE 754-2008, which standardized decimal floating-point. This paper presents a new architecture for two operand and multi-operand signed-digit decimal addition. Signed-digit architectures are advantageous because there are no carry-propagate chains. The proposed signed-digit adder reduces the critical path delay by parallelizing the correction stage inherent to decimal addition. For performance evaluation, we synthesize and compare multiple unsigned and signed-digit multi-operand decimal adder architectures on 0.18μm CMOS VLSI technology. Synthesis results for 2, 4, 8, and 16 operands with 8 decimal digits provide critical data in determining each adder's performance and scalability.

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Fast Signed-Digit Multi-operand Decimal Adders

This work presents a new classifier architecture for Non-Destructive Evaluation (NDE) applications which can robustly detect the presence and location of material flaws, by combining an Adaptive Noise Cancellation (ANC) technique with a stacked ensemble of neural networks. The ANC is utilized as a preprocessing unit to reduce interference and enhance a flaw echo. The ANC output data is the input to a 2-level ensemble neural network which makes a final flaw detection prediction. Initial simulation results show that the proposed architecture can offer highly reliable flaw detection and localization, with significant Flaw to Clutter Ratio (FCR) enhancements.

Erdal Oruklu

Papers

Recursive filters for subband decomposition algorithms in ultrasonic detection applications

An efficient hardware design tool for scalable matrix multiplication

Investigation of Feature Inputs for Binary Classification of Ultrasonic NDT Signals using SVM and Neural Networks

Fast Signed-Digit Multi-operand Decimal Adders

A Stacked Ensemble Neural Network Classifier for Ultrasonic Non-Destructive Evaluation Applications