Digital cameras have become increasingly popular over the last few years. Digital imagingtechnology has grown to new markets including cellular phones and PDA devices. With thediverse marketplace, a variety of imaging technology must be available. Imaging technologyhas expanded to include both charge-coupled device (CCD) and CMOS image Sensors.
為了提高直接轉矩控制（DTC）系統定子磁鏈估計精度，降低電流、電壓測量的隨機誤差，提出了一種基于擴展卡爾曼濾波（EKF）實現異步電機轉子位置和速度估計的方法。擴展卡爾曼濾波器是建立在基于旋轉坐標系下由定子電流、電壓、轉子轉速和其它電機參量所構成的電機模型上，將定子電流、定子磁鏈、轉速和轉子角位置作為狀態變量，定子電壓為輸入變量，定子電流為輸出變量，通過對磁鏈和轉速的閉環控制提高定子磁鏈的估計精度，實現了異步電機的無速度傳感器直接轉矩控制策略，仿真結果驗證了該方法的可行性,提高了直接轉矩的控制性能。
Abstract:
In order to improve the Direct Torque Control（DTC） system of stator flux estimation accuracy and reduce the current， voltage measurement of random error， a novel method to estimate the speed and rotor position of asynchronous motor based on extended Kalman filter was introduced. EKF was based on d-p axis motor and other motor parameters （state vector: stator current， stator flux linkage， rotor angular speed and position； input: stator voltage； output: staror current). EKF was designed for stator flux and rotor speed estimation in close-loop control. It can improve the estimated accuracy of stator flux. It is possible to estimate the speed and rotor position and implement asynchronous motor drives without position and speed Sensors. The simulation results show it is efficient and improves the control performance.
Delta-sigma ADCs, with their high accuracy and high noiseimmunity, are ideal for directly measuring many typesof Sensors. Nevertheless, input sampling currents canoverwhelm high source impedances or low-bandwidth,micropower signal conditioning circuits. The LTC®2484family of delta sigma converters solves this problem bybalancing the input currents, thussimplifying or eliminatingthe need for signal conditioning circuits.
Rotating shafts experience a an elliptical motion called whirl. It is important to decompose this motion into a forward and backward whil orbits. The current function makes use of two Sensors to generate a bi-directional spectrogram. The method can be extended to any time-frequency distribution
％
％ compute the forward/backward Campbell/specgtrogram
％
％ INPUT:
％ y (n x 2) each column is measured from a different sensor
％ ///////
％ __
％ |s1| y(:,1)
％ |__|
％ __
％ / \ ________|/
％ | | | s2 |/ y(:,2)
％ \____/ --------|/
％
％ Fs Sampling frequnecy
％
％ OUTPUT:
％ B spectrogram/Campbel diagram
％ x x-axis coordinate vector (time or Speed)
％ y y-axis coordinate vector (frequency [Hz])
The Network Security Response Framework (NSRF) allows for testing different computer security response engines and methodologies. It supports simulated and real: Intrusion Detection Systems (Sensors), Attacks, and Responses.
we present real-time particle filters, which make use of all sensor information even when the filter update rate is below the update rate of the Sensors.
這篇英文論文主要介紹了基于TI公司TMS320C24x的直流無刷電機控制。A complete solution proposal is
presented below: control structures, power hardware topology, shaft
position Sensors, control hardware and remarks on energy conversion
efficiency can be found in this document.
This paper presents a visual based localization
mechanism for a legged robot. Our proposal, fundamented
on a probabilistic approach, uses a precompiled topological
map where natural landmarks like doors or ceiling lights
are recognized by the robot using its on-board camera.
Experiments have been conducted using the AIBO Sony
robotic dog showing that it is able to deal with noisy Sensors
like vision and to approximate world models representing
indoor ofce environments. The two major contributions of
this work are the use of this technique in legged robots, and
the use of an active camera as the main sensor
This approach addresses two difficulties simultaneously: 1)
the range limitation of mobile robot Sensors and 2) the difficulty of detecting buildings in
monocular aerial images. With the suggested method building outlines can be detected
faster than the mobile robot can explore the area by itself, giving the robot an ability to
“see” around corners. At the same time, the approach can compensate for the absence
of elevation data in segmentation of aerial images. Our experiments demonstrate that
ground-level semantic information (wall estimates) allows to focus the segmentation of
the aerial image to find buildings and produce a ground-level semantic map that covers
a larger area than can be built using the onboard Sensors.
