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

This paper describes the communication challenges associated with implementation of a multi-sensor artificial pancreas system on a smartphone device and presents solutions for potential communication losses. In particular, a standardized development framework on an Android device supporting multiple sensors is introduced in order to facilitate a multi-variable closed-loop AP algorithm. Package losses and communication link errors pose major challenges for the AP systems to overcome. The proposed solution offers a conservative model, wherein the controller application on the host smartphone requests status updates, resending of packages if necessary, and ultimately, warnings are issued to the user when data cannot be recovered.

Communication challenges in a multi-sensor closed-loop artificial pancreas system

This paper presents two computer architecture mechanisms that can be employed together to allow programs to be written where their individual instructions can (1) be executed in parallel in separate processors and (2) be arbitrarily assigned to be executed by any processor in a connected fabric of processors. This is an emerging parallel execution technique for future high performance and embedded computers. Changing the placement of a program's instructions in a processing fabric is equivalent to rearranging the computer architecture around the program itself. Reasons to change the computer architecture around the software are to improve overall execution speed and to perform load balancing. The two computer architecture mechanisms are (1) a custom dataflow instruction set and (2) a processor block called an operation cell. The most important feature of the instruction set is the high level of operational independence of the instructions (i.e. no shared memory/registers between instructions). The operation cell is a processor construct that stores an instruction and manages the execution life of that instruction. An operation cell either executes the instruction itself or forwards the execution to another operation cell in a different processor.

Fine-Grained Instruction Placement in Polymorphic Computing Architectures

Designing a universal embedded hardware architecture for discrete wavelet transform (DWT) is a challenging problem due to the diversity among wavelet kernel filters. In this work, we present three different hardware architectures for implementing multiple wavelet kernels. The first scheme utilizes fixed, parallel hardware for all the required wavelet kernels whereas the second scheme employs a processing element (PE) based datapath that can be configured for multiple wavelet filters during run-time. The third scheme makes use of partial runtime configuration of FPGA units for dynamically programming any desired wavelet filter. We present FPGA synthesis results for simultaneous implementation of six different wavelets for the proposed methods. Performance analysis and comparison of area, timing and power results are presented for the Virtex-II Pro FPGA implementation.

Universal Wavelet Kernel Implementation Using Reconfigurable Hardware

Chirp pulses are often used as an effective technique for ultrasonic imaging in NDE or flaw detection applications. Chirp echoes characterize dispersive media while enabling pulse compression and improved echo detection. Characterizing defects in large grain materials is a complex and challenging problem in ultrasonic imaging applications. The complexity is caused by microstructure scattering which often results in interference and masking of ultrasonic flaw echoes. Therefore methods to unravel these complex signals are advantageous. In this investigation, a computationally efficient method for parameter estimation of ultrasonic signals, consisting multiple chirplet echoes is introduced. Analytical results show that the algorithm is efficient and successful in signal representation.

A computationally efficient algorithm for ultrasonic signal decomposition and flaw detection

This chapter presents a reconfigurable ultrasonic smart sensor platform (RUSSP) for real-time signal analysis and image processing. The RUSSP is designed to facilitate the development and implementation of ultrasonic signal processing algorithms with embedded software and reconfigurable hardware. It provides the opportunity to explore the full design space including software only, hardware only, and hardware/software codesign architectures. Multiple case studies are investigated including real-time ultrasonic flaw detection, signal decomposition, and parameter estimation on the RUSSP, which hosts a Linux operating system and reconfigurable fabric.

Erdal Oruklu

Papers

Communication challenges in a multi-sensor closed-loop artificial pancreas system

Fine-Grained Instruction Placement in Polymorphic Computing Architectures

Universal Wavelet Kernel Implementation Using Reconfigurable Hardware

A computationally efficient algorithm for ultrasonic signal decomposition and flaw detection

Reconfigurable ultrasonic smart sensor platform for nondestructive evaluation and imaging applications