/pdf/convex-analysisnoer-san-nojin-zhan-nituite-5dl2rbmoyr.pdf

Convex Analysisの二,三の進展について

Praise for the Third Edition: "This is one of the best books available. Its excellent organizational structure allows quick reference to specific models and its clear presentation . . . solidifies the understanding of the concepts being presented."IIE Transactions on Operations EngineeringThoroughly revised and expanded to reflect the latest developments in the field, Fundamentals of Queueing Theory, Fourth Edition continues to present the basic statistical principles that are necessary to analyze the probabilistic nature of queues. Rather than presenting a narrow focus on the subject, this update illustrates the wide-reaching, fundamental concepts in queueing theory and its applications to diverse areas such as computer science, engineering, business, and operations research.This update takes a numerical approach to understanding and making probable estimations relating to queues, with a comprehensive outline of simple and more advanced queueing models. Newly featured topics of the Fourth Edition include:Retrial queuesApproximations for queueing networksNumerical inversion of transformsDetermining the appropriate number of servers to balance quality and cost of serviceEach chapter provides a self-contained presentation of key concepts and formulae, allowing readers to work with each section independently, while a summary table at the end of the book outlines the types of queues that have been discussed and their results. In addition, two new appendices have been added, discussing transforms and generating functions as well as the fundamentals of differential and difference equations. New examples are now included along with problems that incorporate QtsPlus software, which is freely available via the book's related Web site.With its accessible style and wealth of real-world examples, Fundamentals of Queueing Theory, Fourth Edition is an ideal book for courses on queueing theory at the upper-undergraduate and graduate levels. It is also a valuable resource for researchers and practitioners who analyze congestion in the fields of telecommunications, transportation, aviation, and management science.

Fundamentals of Queueing Theory

Computer and Robot Vision Vol. 1, by R.M. Haralick and Linda G. Shapiro, Addison-Wesley, 1992, ISBN 0-201-10887-1.

Computer and Robot Vision

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[서평]「Computer Organization and Design, The Hardware/Software Interface」

A modular intelligent transportation system, comprising an environmentally protected enclosure, a system communications bus, a processor module, communicating with said bus, having a image data input and an audio input, the processor module analyzing the image data and/or audio input for data patterns represented therein, having at least one available option slot, a power supply, and a communication link for external communications, in which at least one available option slot can be occupied by a wireless local area network access point, having a communications path between said communications link and said wireless access point, or other modular components.

Modular intelligent transportation system

Closing the gap between the hardware requirements of state-of-the-art convolutional neural networks and the limited resources constraining embedded applications is the next big challenge in deep learning research. The computational complexity and memory footprint of such neural networks are typically daunting for deployment in resource constrained environments. Model compression techniques, such as pruning, are emphasized among other optimization methods for solving this problem. Most existing techniques require domain expertise or result in irregular sparse representations, which increase the burden of deploying deep learning applications on embedded hardware accelerators. In this paper, we propose the autoencoder-based low-rank filter-sharing technique technique (ALF). When applied to various networks, ALF is compared to state-of-the-art pruning methods, demonstrating its efficient compression capabilities on theoretical metrics as well as on an accurate, deterministic hardware-model. In our experiments, ALF showed a reduction of 70\% in network parameters, 61\% in operations and 41\% in execution time, with minimal loss in accuracy.

ALF: Autoencoder-based Low-rank Filter-sharing for Efficient Convolutional Neural Networks

Either or both video and audio data are digitized and their characteristics extracted using a suitable analysis algorithm to obtain at least one descriptor which is sent to a database. The database is searched for information through an query based on the descriptor. An independent claim is also included for an electronic storage medium containing program code for performing the method.

Query preparation and rendering for procuring information from multimedia databases involves searching information from database through query that is based on descriptor of digitized data

Data values in memory have one or more predetermined neighbor relationships and are stored in a multitude of hierarchical last in first out memory (HLIFO) stacks in different hierarchical steps. To process the data a propagation process is used starting with a first memory address stored in an address LIFO and continuing until no more addresses are available or until a stop criterion is fulfilled.

Method for processing image data values stored in a hierarchical last in first out memory, for use in imaging analysis, coding and compression prior to transmission and archiving, etc.

This paper addresses disparity estimation from image pairs by error minimization. When taken from a monocular camera pan sequence, those images often suffer from luminance variations, caused by the spatially and temporally separated camera positions. Therefore, here we discuss and analyze an existing correction approach for the block matching algorithm. From the observations made we derive a new approach for illumination correction of local error minimization in disparity space. With its simplicity, this multiple disparity space prescaling not only provides the high speed needed for mobile devices, but also decreases the number of false matches by an a priori restriction of correction values.

Luminance Correction in Stereo Correspondence Based Structure from Motion

Summary form only given. Future automotive security systems will benefit from visual scene analysis based on a fusion of video, infrared, and radar images. Today we have already functions like lane departure warning and automatic cruise control (ACC) for pretty well defined driving environments, such as highways and primary roads. Recent research activities concentrate on more complex environments, such as city traffic with a wide variety of traffic participants moving in an unpredictable manner, e.g. bikes, pedestrians, children, and even animals, and under changing weather and lighting conditions. The ITRS semiconductor roadmap for microelectronics forecasts a continued doubling of transistor capacity per chip every 2 to 2.5 years enabling billion transistor ASIC designs in the near future. Multi processor system on chip (MPSoC) solutions with 8, 16 or even more standard RISC CPU cores, mega-bytes of fast (ns access latencies) on-chip SRAM memories, giga-byte per second interconnect buses or NoC (network on chip) meshes, high-speed serial I/Os and, last but not least, million gate equivalent dedicated hardware accelerator functions in eFPGA (embedded field programmable gate array) logic are becoming reality on a single silicon substrate. Examples of current research projects shall illustrate our perception on how this tremendous increase in functionality and computational performance per chip area may impact automotive control unit (ACU) architectures for driver assistance applications. The AutoVision processor is a dynamically reconfigurable MPSoC prototype where video-specific pixel processing engines are on-the-fly loaded or exchanged without interrupting regular system operations. For the time being, pixel processing engines cover functions such as object edge detection or luminance segmentation, and are implemented as dedicated hardware accelerators to ensure real-time frame processing capabilities of the AutoVision processor. Dynamic replacement of processing engines ensures an automatic and area efficient adaptation to various driving conditions. Segmented objects are, in a subsequent step, characterized by means of standard MPEG-7 descriptors and entered as search criteria into traffic scene analysis databases. Goal is to obtain a clean distinction between passenger cars, trucks, and big rectangular traffic signs, and to identify pedestrians or bikers in complex traffic situations. The AutoVision processor project is supported by the German Research Foundation (DFG) in the special emphasis research programme "reconfigurable computing".

/pdf/autovision-flexible-processor-architecture-for-video-1c5zlxxscg.pdf

Walter Stechele

Papers

ALF: Autoencoder-based Low-rank Filter-sharing for Efficient Convolutional Neural Networks

Query preparation and rendering for procuring information from multimedia databases involves searching information from database through query that is based on descriptor of digitized data

Method for processing image data values stored in a hierarchical last in first out memory, for use in imaging analysis, coding and compression prior to transmission and archiving, etc.

Luminance Correction in Stereo Correspondence Based Structure from Motion

AutoVision - flexible processor architecture for video-assisted driving