Numerical Methods for Scientists and Engineers

electrical and computer engineering ece courses ece 257a multiuser communication systems 4 congestion control convex programming and dual controller fair end end rate allocation max min fair vs proportional, electrical systems engineering washington university arye nehorai eugene and martha lohman professor of electrical engineering phd stanford university signal processing imaging biomedicine communications, ieee transactions on aerospace and electronic systems ieee transactions on aerospace and electronic systems focuses on the organization design development integration and operation of complex systems for space air, department of electrical engineering and computer science h kumar wickramsinghe department chair 2213 engineering hall 949 824 4821 http www eng uci edu dept eecs overview electrical engineering and computer science is, download electrical and electronics engineering ebooks syst mes temps discret commande num rique des proc d s pdf 499 ko terminology and symbols in control engineering pdf 326 ko the best of thomas, publications stream wise list iit kanpur papers published in journals in 2016 dutta s patchaikani p k behera l near optimal controller for nonlinear continuous time systems with unknown dynamics, resolve a doi name type or paste a doi name into the text box click go your browser will take you to a web page url associated with that doi name send questions or comments to doi, peer reviewed journal ijera com international journal of engineering research and applications ijera is an open access online peer reviewed international journal that publishes research, dod sbir 2016 2 sbir gov note the solicitations and topics listed on this site are copies from the various sbir agency solicitations and are not necessarily the latest and most up, an english japanese dictionary of electrical engineering c 2952 9 691 c band c c contact c c maccs centre for mathematical modelling and computer simulation, the of and to a in that is was he for it with as his on be most common text click on the icon to return to www berro com and to enjoy and benefit the of and to a in that is was he for it with as his on be at by i this had

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.

/pdf/a-survey-of-applications-and-human-motion-recognition-with-hags1y076t.pdf

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 era where the growth of computer performance is significantly flattening, new parallel computer architectures are needed to continue to fuel that growth. One candidate architecture to fuel this growth is a recently proposed dataflow processor that is meant to be tiled together into a fabric to create a polymorphic computer (i.e. a reconfigurable computer whose architecture can be changed to match the software) where program instructions can be independently migrated in a fine-grained manner around the fabric to logically change the computer architecture. This work provides a summary of the dataflow processor’s instruction set and details how to write programs for this processor. The techniques that are presented to write programs for this processor are diagramming and 12 building block programming patterns. The 12 patterns are: Operations with a Constant, Simple Copy, Pitch (Discard), Counters, Critical Sections, Funnels (Gathers), Distribution Tree (Scatters), Lookup Tables, Single Value Selection, Single Value Steering, Switch (Multi-Value Steering), and Loops. These patterns are expected to benefit future compilers. In addition, the ideal timing results of several test algorithms that are implemented in this processor and that utilize the above patterns are presented.

Program diagramming and fundamental programming patterns for a polymorphic computing dataflow processor

This work presents a classifier architecture for Non-Destructive Evaluation (NDE) applications which can robustly detect the presence and location of flaws using an ensemble of deep learning networks. The ensemble draws upon the effective sequential time analysis of Long Short-Term Memory - Neural Networks (LSTM–NN), the function estimation and prediction properties of Wavelet Neural Networks (WNN), and the feature extraction capabilities of Convolution Neural Networks (CNN). Simulation results confirm that the proposed architecture offers highly reliable flaw detection and localization with significant Flaw to Clutter Ratio (FCR) enhancements.

A New Classifier Network for Ultrasonic NDE Applications based on Ensemble Deep Learning

Matrix multiplication operations are heavily used in communication systems, video, signal and image processing applications such as echo cancellation, adaptive beamforming, and Multiple-Input Multiple-Output (MIMO) systems, and are also used in matrix factorizations such as Cholesky, LU, QR and DCT. However, it is challenging to implement a high speed matrix multiplication operator for large matrices due to the fact that the number of multiplication operations grows rapidly with functions of n3. This paper presents the implementation of a reconfigurable pipelined high speed and high precision matrix multiplication module designer for large matrices on Xilinx Virtex-5 and Spartan 3E FPGAs using high speed memory interface for data transfers.

High performance reconfigurable pipelined matrix multiplication module designer

In this work, an artificial pancreas (AP) system, implemented on a mobile device is described. The proposed AP platform integrates hardware (insulin pump, glucose monitor, various sensors for vital signs and physical activities) and software (closed-loop control algorithm, sensor fusion, data storage and remote server access) components via smartphone that is running a dedicated Operating System designed for AP systems. Interfacing with this OS and custom application development steps are presented. Closed loop operation is demonstrated with case studies.

Implementation of an artificial pancreas system on a mobile device

A hardware system entrusted with security is referred to as the trusted platform module (TPM) which is available for various processor architectures. The two important processor architectures which account for most of general computing systems are based on ARM and x86 processors. The ARM processors have a TPM referred to as TrustZone architecture. The x86 systems' security directives are dictated by the Trusted Computing Group (TCG) which defines the TPM coprocessor features. In this paper, we compare these two approaches of TPM architectures. An effective TPM needs to be adaptive as threats evolve. Threats can arise from firmware bugs in the TPM or decay in the complexity of ciphering algorithms. Therefore, we propose adaptive TPM architecture to counter evolving threats using an FPGA block to alter and patch firmware and change ciphering systems. This along with the one to one association of an explicit security policy with threat is shown to be a powerful counter towards evolving threats.

Erdal Oruklu

Papers

Program diagramming and fundamental programming patterns for a polymorphic computing dataflow processor

A New Classifier Network for Ultrasonic NDE Applications based on Ensemble Deep Learning

High performance reconfigurable pipelined matrix multiplication module designer

Implementation of an artificial pancreas system on a mobile device

Threat-adaptive architectures for trusted platform modules in secure computing systems