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 introduces a platform capable of handling varying operand sizes across a set of algorithms to be accelerated by a low power hardware core. The key to dealing with different levels of operand granularity is to implement them on the reconfigurable distributed processing network (RDPN), which can handle dynamic changes to operand size. This approach allows algorithms which may be incompatible for the same hardware platform to be accelerated by a common hardware core. This acceleration core based on the RDPN allows algorithms of different levels in operand granularity to be accelerated by a low power system without the need for extra logic. The RDPN architecture provides a flexible platform, which bridges the gap between software and hardware by extending the applicability of an embedded hardware system. This extended applicability is a result of the native support for word size extension and contraction within the RDPN computational fabric. A case study from the signal processing algorithmic domain will be presented to show how the RDPN fabric dynamically adapts to the operand granularity required by different stages of an algorithm

Dynamic reconfigurable distributed processing network with dual levels of operand granularity

An artificial pancreas system is implemented as a mobile application which connects a glucose sensor, an insulin pump and wearable physical activity sensors. It automatically delivers the optimal insulin amounts, based on a multivariable control algorithm. The algorithm, previously tested on a laptop, is to be hosted on a smartphone. This requires a fast, reliable method to translate the inherent functions. Validation tests of the new mobile application confirm there are no deviations from the original code.

From Design to Implementation - Algorithm Conversion and Transition to a Mobile Platform for an Artificial Pancreas

In many NDE applications, accurate localization of Ultrasonic echoes can be an important first step for further characterization tasks, including estimation, detection and classification. This is especially critical for a generalized classifier pipeline which can handle multi-class flaw analysis without additional supervised training steps. Therefore, the purpose of this study is to find a localization algorithm which can localize and find a bounding box for reflections of a void (flaw) in raw ultrasonic B-scan data. The proposed Grid Based Localization (GBL) algorithm is a regression method which uses Deep Learning (DL) to find the bounding box for the grid structure. Results demonstrate more robust performance than conventional contour-based techniques and the algorithm is suitable for combining with generalized DL classifier algorithms such as Meta Learning.

Erdal Oruklu

Papers

Dynamic reconfigurable distributed processing network with dual levels of operand granularity

From Design to Implementation - Algorithm Conversion and Transition to a Mobile Platform for an Artificial Pancreas

Localization of Ultrasonic Flaws using Grid Based Deep Learning