Online Scanner的問題，透過圖書和論文來找解法和答案更準確安心。 我們找到下列特價商品、必買資訊和推薦清單

Big Data for Twenty-First-Century Economic Statistics, 79

為了解決Online Scanner的問題，作者 這樣論述：

The papers in this volume analyze the deployment of Big Data to solve both existing and novel challenges in economic measurement. The existing infrastructure for the production of key economic statistics relies heavily on data collected through sample surveys and periodic censuses, together with

administrative records generated in connection with tax administration. The increasing difficulty of obtaining survey and census responses threatens the viability of existing data collection approaches. The growing availability of new sources of Big Data--such as scanner data on purchases, credit c

ard transaction records, payroll information, and prices of various goods scraped from the websites of online sellers--has changed the data landscape. These new sources of data hold the promise of allowing the statistical agencies to produce more accurate, more disaggregated, and more timely economi

c data to meet the needs of policymakers and other data users. This volume documents progress made toward that goal and the challenges to be overcome to realize the full potential of Big Data in the production of economic statistics. It describes the deployment of Big Data to solve both existing and

novel challenges in economic measurement, and it will be of interest to statistical agency staff, academic researchers, and serious users of economic statistics.

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基於StyleGAN-v2並強化人臉特徵一致性之功能性磁振造影人臉影像重建

為了解決Online Scanner的問題，作者田晏瑜 這樣論述：

功能性磁振造影（fMRI）是一種非侵入性的大腦功能造影工具，其原理是量測基於神經元活動引起的血氧濃度變化而出現的微小的磁場差異來表示大腦各部分組織的活化狀態。當人類的視網膜上的視覺受器接收到刺激時，經過視覺系統傳遞到大腦中處理視覺訊號的區域，而不同的刺激會引發不同的活化反應。我們期望能夠找到刺激曹料與腦部反應的相關性，並重現出受試者在視覺刺激實驗中看見之人臉影像。在此研究中，我們對腦部反應訊號進行相對應的視覺影像重建。本論文以開源資料集進行模型訓練和測試。而我們提出的重建方法可分為兩個部分，第一部分是將腦部反應訊號映射到人臉影像生成器的樣本空間再透過預先訓練完成的生成器將人臉影像重建。而第二

部分則是解析腦部反應訊號中包含的刺激材料屬性，透過調整映射後的樣本空間使重建影像具備更高的屬性一致性。最後，人臉影像的重建結果我們亦分為兩個部分討論。首先是針對重建影像與原始的刺激影像中屬性一致性的正確率，經過分析與調整的屬性在最後的重建結果都有顯著的提高。其次是以線上問卷的方式，讓人們以刺激影像為基準，在正確的重建影像和任意的其他重建影像中選出較為相似的選項。而問卷的的結果顯示答題的正確率為90\%。此研究透過調整樣本空間改變影像的面部特徵使重建的人臉影像除了具有相似的外貌之外亦可以包含更精確的特徵。未來亦可經由對大腦更深入的解析並取得更多的特徵資訊使重建影像的品質提升。

Field and Service Robotics: Results of the 7th International Conference

為了解決Online Scanner的問題，作者 這樣論述：

Mechanism Design.- Terrain Modeling and Following Using a Compliant Manipulator for Humanitarian Demining Applications.- Towards Autonomous Wheelchair Systems in Urban Environments.- Tethered Detachable Hook for the Spiderman Locomotion (Design of the Hook and Its Launching Winch).- New Measurement

Concept for Forest Harvester Head.- Expliner - Toward a Practical Robot for Inspection of High-Voltage Lines.- Perception and Control.- Experimental Study of an Optimal-Control- Based Framework for Trajectory Planning, Threat Assessment, and Semi-Autonomous Control of Passenger Vehicles in Hazard Av

oidance Scenarios.- Receding Horizon Model-Predictive Control for Mobile Robot Navigation of Intricate Paths.- Posterior Probability Estimation Techniques Embedded in a Bayes Filter for Vibration-Based Terrain Classification.- Towards Visual Arctic Terrain Assessment.- Tracking and Servoing.- Pedest

rian Detection and Tracking Using Three-Dimensional LADAR Data.- Passive, Long-Range Detection of Aircraft: Towards a Field Deployable Sense and Avoid System.- Multiclass Multimodal Detection and Tracking in Urban Environments .- Vision-Based Vehicle Trajectory Following with Constant Time Delay.- L

ocalization.- Radar Scan Matching SLAM Using the Fourier-Mellin Transform.- An Automated Asset Locating System (AALS) with Applications to Inventory Management.- Active SLAM and Loop Prediction with the Segmented Map Using Simplified Models.- Outdoor Downward-Facing Optical Flow Odometry with Commod

ity Sensors.- Place Recognition Using Regional Point Descriptors for 3D Mapping.- Mapping.- Scan-Point Planning and 3-D Map Building for a 3-D Laser Range Scanner in an Outdoor Environment.- Image and Sparse Laser Fusion for Dense Scene Reconstruction.- Relative Motion Threshold for Rejection in ICP

Registration.- Bandit-Based Online Candidate Selection for Adjustable Autonomy.- Applied Imitation Learning for Autonomous Navigation in Complex Natural Terrain.- Underwater Localization and Mapping.- Trajectory Design for Autonomous Underwater Vehicles Based on Ocean Model Predictions for Feature

Tracking.- AUV Benthic Habitat Mapping in South Eastern Tasmania.- Sensor Network Based AUV Localisation.- Experiments in Visual Localisation around Underwater Structures.- Multi-Robot Cooperation.- Leap-Frog Path Design for Multi-Robot Cooperative Localization.- A Location-Based Algorithm for Multi

-Hopping State Estimates within a Distributed Robot Team.- Cooperative AUV Navigation Using a Single Surface Craft.- Multi-Robot Fire Searching in Unknown Environment.- Human Robot Interaction.- Using Virtual Articulations to Operate High-DoF Inspection and Manipulation Motions.- Field Experiment on

Multiple Mobile Robots Conducted in an Underground Mall.- Learning to Identify Users and Predict Their Destination in a Robotic Guidance Application.- Long Term Learning and Online Robot Behavior Adaptation for Individuals with Physical and Cognitive Impairments.- Mining Robotics.- Swing Trajectory

Control for Large Excavators.- The Development of a Telerobotic Rock Breaker.- Camera and LIDAR Fusion for Mapping of Actively Illuminated Subterranean Voids.- Maritime Robotics.- A Communication Framework for Cost-Effective Operation of AUVs in Coastal Regions.- Multi-Robot Collaboration with Rang

e-Limited Communication: Experiments with Two Underactuated ASVs.- A Simple Reactive Obstacle Avoidance Algorithm and Its Application in Singapore Harbor.- Planetary Robotics.- Model Predictive Control for Mobile Robots with Actively Reconfigurable Chassis.- Turning Efficiency Prediction for Skid St

eer Robots Using Single Wheel Testing.- Field Experiments in Mobility and Navigation with a Lunar Rover Prototype.- Rover-Based Surface and Subsurface Modeling for Planetary Exploration.

衛星失效區域定位方法

為了解決Online Scanner的問題，作者王嘉誠 這樣論述：

Contents iList of Tables vList of Figures vi1 Introduction 12 Background and Related Works 32.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32.1.1 Road layer determination . . . . . . . . . . . . . . . . . . . . . . . . . 32.1.2 Positioing in sheltered environ

ment . . . . . . . . . . . . . . . . . . . 62.2 Related Works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82.2.1 Road layer determination . . . . . . . . . . . . . . . . . . . . . . . . . 82.2.2 Positioning in GNSS-denied environments . . . . . . . . . . . . . . . 122.2.3 M

agnetic field positioning . . . . . . . . . . . . . . . . . . . . . . . . 132.2.4 Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142.3 Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163 Preliminary experiment toward various impact fac

tor 183.1 Barometric impact factor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183.1.1 Preliminary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183.1.2 Precision and accuracy of the air-pressure sensors in smartphones . . . 253.1.2.1 Static experiment . . . . . . . .

. . . . . . . . . . . . . . . 263.1.2.2 Dynamic experiment . . . . . . . . . . . . . . . . . . . . . 273.1.3 Impact of Weather . . . . . . . . . . . . . . . . . . . . . . . . . . . . 283.1.4 Impact of driving environment . . . . . . . . . . . . . . . . . . . . . . 313.1.4.1 External temperature eff

ect . . . . . . . . . . . . . . . . . . 313.1.4.2 Internal temperature effect . . . . . . . . . . . . . . . . . . . 323.1.4.3 Speed effect . . . . . . . . . . . . . . . . . . . . . . . . . . 333.1.4.4 Impact of surrounding vehicles . . . . . . . . . . . . . . . . 373.1.5 Impact of air conditioning .

. . . . . . . . . . . . . . . . . . . . . . . 383.1.6 The combination of all factors . . . . . . . . . . . . . . . . . . . . . . 393.2 Magnetic field impact factor . . . . . . . . . . . . . . . . . . . . . . . . . . . 403.2.1 Sensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

. 403.2.1.1 Orientation . . . . . . . . . . . . . . . . . . . . . . . . . . . 403.2.1.2 Sensor drift . . . . . . . . . . . . . . . . . . . . . . . . . . 413.2.1.3 Smartphones . . . . . . . . . . . . . . . . . . . . . . . . . . 413.2.2 Vehicles . . . . . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . . 433.2.2.1 Charging . . . . . . . . . . . . . . . . . . . . . . . . . . . . 433.2.2.2 In-car electrical appliances . . . . . . . . . . . . . . . . . . 443.2.2.3 Vehicle types . . . . . . . . . . . . . . . . . . . . . . . . . . 453.2.2.4 Nearby vehicles . . . . . . . . . . . . . . . . . .

. . . . . . 463.2.3 Magnetic field variations . . . . . . . . . . . . . . . . . . . . . . . . . 484 Proposed method in GNSS-denied environment 514.1 Proposed BARLD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 514.1.1 Database . . . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . . . 524.1.2 Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 524.1.3 Initial level determination . . . . . . . . . . . . . . . . . . . . . . . . 534.1.4 Multi-upper levels within the range d1 . . . . . . . . . . . . . . . . . . 544.1.4.1 Connected ramps or roads

are not parallel . . . . . . . . . . 544.1.4.2 Ramps are parallel but with a height difference . . . . . . . . 544.2 Proposed MVP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 554.2.1 Accuracy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 554.2.2 Positioning

speed (delay) . . . . . . . . . . . . . . . . . . . . . . . . . 574.2.3 Proposed MVP algorithm . . . . . . . . . . . . . . . . . . . . . . . . 584.2.4 Robustness to phone orientation . . . . . . . . . . . . . . . . . . . . . 604.2.5 Magnetic field map (ground truth) . . . . . . . . . . . . . . . .

. . . . 604.2.5.1 Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . 614.2.5.2 Implementation . . . . . . . . . . . . . . . . . . . . . . . . 624.2.6 INS-based positioning system . . . . . . . . . . . . . . . . . . . . . . 635 Evaluation and Discussion 655.1 Road layer determination . .

. . . . . . . . . . . . . . . . . . . . . . . . . . . 655.1.1 Threshold (δ) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 665.1.2 Sampling rate (R) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 685.1.3 Activation Range (d1) . . . . . . . . . . . . . . . . . . . . . . . .

. . 705.1.4 Large-scale Road test . . . . . . . . . . . . . . . . . . . . . . . . . . . 725.2 Road tests in different tunnels . . . . . . . . . . . . . . . . . . . . . . . . . . 735.2.1 Accuracy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73iii5.2.2 Lane determination . . . .

. . . . . . . . . . . . . . . . . . . . . . . . 745.2.3 Positioning speed (delay) . . . . . . . . . . . . . . . . . . . . . . . . . 755.2.4 Cost . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 775.3 Large-scale real-road tests . . . . . . . . . . . . . . . . . . . . . . . . .

. . . 775.3.1 Accuracy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 785.3.2 Lane determination . . . . . . . . . . . . . . . . . . . . . . . . . . . . 785.3.3 Positioning speed (delay) . . . . . . . . . . . . . . . . . . . . . . . . . 795.3.4 Car orientation variations . . . .

. . . . . . . . . . . . . . . . . . . . . 815.3.5 High speed and low sampling rate . . . . . . . . . . . . . . . . . . . . 815.3.6 Traffic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 825.3.7 Bridges and parking garages . . . . . . . . . . . . . . . . . . . . . . . 825.4 Dis

cussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 835.4.1 Road layer determination . . . . . . . . . . . . . . . . . . . . . . . . . 835.4.2 Positioning in sheltering environment . . . . . . . . . . . . . . . . . . 846 Conclusion 86Bibliography 87