This application provides much functionality for creating data-driven reports, including preview, graphics, and formatting. There is no documentation so you may have to work with it awhile, but there is a demo on how to use it, and the functionality is rich.
壓縮包中有5篇論文，分別為《data-driven analysis of variables and dependencies in continuous optimization problems and EDAs》這是一篇博士論文，較為詳細(xì)的介紹了各種EDA算法；《Anisotropic adaptive variance scaling for Gaussian estimation of distribution algorithm》《Enhancing Gaussian Estimation of Distribution Algorithm by Exploiting Evolution Direction with Archive》《Niching an Archive-based Gaussian Estimation of Distribution Algorithm via Adaptive Clustering》《Supplementary material for Enhancing Gaussian Estimation of Distribution Algorithm by Exploiting Evolution Direction with Archive》《基于一般二階混合矩的高斯分布估計(jì)算法》介紹了一些基于EDA的創(chuàng)新算法。
Convergence between the two largest networks (Telecom and IP) is taking place
very rapidly and at diff erent levels: (1) network level: unifi cation of IP networks
with traditional Telecom networks through evolving standards (Session Initiation
Protocol (SIP), Realtime Transfer Protocol (RTP), SS7, 3G) to support interopera-
bility; (2) service level: traditional Telecom services like voice calls are being provi-
sioned on the IP backbone (VoIP), while traditional IP services (most data-driven
services such as multimedia, browsing, chatting, gaming, etc.) are accessible over
the Telecom network.
In this research, we have designed, developed implemented a wireless sensor
networks based smart home for safe, sound and secured living environment for
any inhabitant especially elderly living alone. We have explored a methodology
for the development of efficient electronic real time data processing system to
recognize the behaviour of an elderly person. The ability to determine the
wellness of an elderly person living alone in their own home using a robust,
flexible and data driven artificially intelligent system has been investigated. A
framework integrating temporal and spatial contextual information for
determining the wellness of an elderly person has been modelled. A novel
behaviour detection process based on the observed sensor data in performing
essential daily activities has been designed and developed.
Low power operation of electronic apparatus has becomeincreasingly desirable. Medical, remote data acquisition,power monitoring and other applications are good candidatesfor battery driven, low power operation. Micropoweranalog circuits for transducer-based signal conditioningpresent a special class of problems. Although micropowerICs are available, the interconnection of these devices toform a functioning micropower circuit requires care. (SeeBox Sections, “Some Guidelines for Micropower Designand an Example” and “Parasitic Effects of Test Equipmenton Micropower Circuits.”) In particular, trade-offs betweensignal levels and power dissipation become painful whenperformance in the 10-bit to 12-bit area is desirable.
This application note covers the design considerations of a system using the performance
features of the LogiCORE™ IP Advanced eXtensible Interface (AXI) Interconnect core. The
design focuses on high system throughput through the AXI Interconnect core with F
MAX
and
area optimizations in certain portions of the design.
The design uses five AXI video direct memory access (VDMA) engines to simultaneously move
10 streams (five transmit video streams and five receive video streams), each in 1920 x 1080p
format, 60 Hz refresh rate, and up to 32 data bits per pixel. Each VDMA is driven from a video
test pattern generator (TPG) with a video timing controller (VTC) block to set up the necessary
video timing signals. Data read by each AXI VDMA is sent to a common on-screen display
(OSD) core capable of multiplexing or overlaying multiple video streams to a single output video
stream. The output of the OSD core drives the DVI video display interface on the board.
Performance monitor blocks are added to capture performance data. All 10 video streams
moved by the AXI VDMA blocks are buffered through a shared DDR3 SDRAM memory and are
controlled by a MicroBlaze™ processor.
The reference system is targeted for the Virtex-6 XC6VLX240TFF1156-1 FPGA on the
Xilinx® ML605 Rev D evaluation board
