Design of vision-based soccer robot using DSP

A new design of vision based soccer robot using the type TMS320F240 of DSPs for MiroSot series is presented. The DSP used enables cost effective control of DC motor, and features fewer external components, lower system cost and better performances than traditional microcontroller. The hardware architecture of robot is firstly presented in detail, and then the software design is briefly discussed. The control structure of decision making subsystem is illuminated also in this paper. The conclusion and prospect are given at last.

Stereo vision based SLAM using Rao-Blackwellised particle filter

We present an algorithm which can realize mobile robot in unknown outdoor environments, which 3D stereo vision simultaneous localization and mapping （SLAM） for means the 6-DOF motion and a sparse but persistent map of natural landmarks be constructed online only with a stereo camera. In mobile robotics research, we extend FastSLAM 2.0 like stereo vision SLAM with ＂pure vision＂ domain to outdoor environments. Unlike popular stochastic motion model used in conventional monocular vision SLAM, we utilize the ideas of structure from motion （SFM） for initial motion estimation, which is more suitable for the robot moving in large-scale outdoor, and textured environments. SIFT features are used as natural landmarks, and its 3D positions are constructed directly through triangulation. Considering the computational complexity and memory consumption, Bkd-tree and Best-Bin-First （BBF） search strategy are utilized for SIFT feature descriptor matching. Results show high accuracy of our algorithm, even in the circumstance of large translation and large rotation movements.

Vision-based Stabilization of Nonholonomic Mobile Robots by Integrating Sliding-mode Control and Adaptive Approach

Vision-based pose stabilization of nonholonomic mobile robots has received extensive attention. At present, most of the solutions of the problem do not take the robot dynamics into account in the controller design, so that these controllers are difficult to realize satisfactory control in practical application. Besides, many of the approaches suffer from the initial speed and torque jump which are not practical in the real world. Considering the kinematics and dynamics, a two-stage visual controller for solving the stabilization problem of a mobile robot is presented, applying the integration of adaptive control, sliding-mode control, and neural dynamics. In the first stage, an adaptive kinematic stabilization controller utilized to generate the command of velocity is developed based on Lyapunov theory. In the second stage, adopting the sliding-mode control approach, a dynamic controller with a variable speed function used to reduce the chattering is designed, which is utilized to generate the command of torque to make the actual velocity of the mobile robot asymptotically reach the desired velocity. Furthermore, to handle the speed and torque jump problems, the neural dynamics model is integrated into the above mentioned controllers. The stability of the proposed control system is analyzed by using Lyapunov theory. Finally, the simulation of the control law is implemented in perturbed case, and the results show that the control scheme can solve the stabilization problem effectively. The proposed control law can solve the speed and torque jump problems, overcome external disturbances, and provide a new solution for the vision-based stabilization of the mobile robot.

A MAP Approach for Vision-based Self-localization of Mobile Robot

一当场，自我本地化系统为在有深入的 3D 里程碑的 3D 环境起作用的活动机器人被开发。机器人通过合并从 odometry 和单向性的照相机收集的信息的一个地图评估者递归地估计它的姿势。我们为这二个传感器造非线性的模型并且坚持说机器人运动和不精密的传感器大小的无常操作应该全部被嵌入并且追踪我们的系统。我们在一个概率的几何学观点和使用 unscented 变换描述无常框架宣传无常，它经历给定的非线性的功能。就我们的机器人的处理力量而言，图象特征在相应投射特征的附近被提取。另外，数据协会被统计距离评估。最后，一系列系统的实验被进行证明我们的系统的可靠、精确的性能。

Night Vision Object Tracking System Using Correlation Aware LSTM-Based Modified Yolo Algorithm

Improved picture quality is critical to the effectiveness of object recog-nition and tracking.The consistency of those photos is impacted by night-video systems because the contrast between high-profile items and different atmospheric conditions,such as mist,fog,dust etc.The pictures then shift in intensity,colour,polarity and consistency.A general challenge for computer vision analyses lies in the horrid appearance of night images in arbitrary illumination and ambient envir-onments.In recent years,target recognition techniques focused on deep learning and machine learning have become standard algorithms for object detection with the exponential growth of computer performance capabilities.However,the iden-tification of objects in the night world also poses further problems because of the distorted backdrop and dim light.The Correlation aware LSTM based YOLO(You Look Only Once)classifier method for exact object recognition and deter-mining its properties under night vision was a major inspiration for this work.In order to create virtual target sets similar to daily environments,we employ night images as inputs;and to obtain high enhanced image using histogram based enhancement and iterative wienerfilter for removing the noise in the image.The process of the feature extraction and feature selection was done for electing the potential features using the Adaptive internal linear embedding(AILE)and uplift linear discriminant analysis(ULDA).The region of interest mask can be segmen-ted using the Recurrent-Phase Level set Segmentation.Finally,we use deep con-volution feature fusion and region of interest pooling to integrate the presently extremely sophisticated quicker Long short term memory based(LSTM)with YOLO method for object tracking system.A range of experimentalfindings demonstrate that our technique achieves high average accuracy with a precision of 99.7%for object detection of SSAN datasets that is considerably more than that of the other standard object detection mechanism.Our approach may therefore satisfy the true demands of night scene target detection applications.We very much believe that our method will help future research.

Vision-based Localization from Three-Line Structures (TLS)

This paper presents a novel vision based localization algorithm from three-line structure ( TLS) .Two types of TLS are investigated: 1) three parallel lines ( Structure I) ; 2) two parallel lines and one orthogonal line ( Structure II) .From single image of either structure,the camera pose can be uniquely computed for vision localization.Contributions of this paper are as follows: 1 ) both TLS structures can be used as simple and practical landmarks,which are widely available in daily life; 2) the proposed algorithm complements existing localization methods,which usually use complex landmarks,especially in the partial blockage conditions; 3) compared with the general Perspective-3-Lines ( P3L) problem,camera pose can be uniquely computed from either structure.The proposed algorithm has been tested with both simulation and real image data.For a typical simulated indoor condition ( 75 cm-size landmark,less than 7.0 m landmark-to-camera distance,and 0.5-pixel image noises) ,the means of localization errors from Structure I and Structure II are less than 3.0 cm.And the standard deviations are less than 3.0 cm and 1.5 cm,respectively.The algorithm is further validated with two actual image experiments.Within a 7.5 m × 7.5 m indoor situation,the overall relative localization errors from Structure I and Structure II are less than 2.2% and 2.3% ,respectively,with about 6.0 m distance.The results demonstrate that the algorithm works well for practical vision localization.

EyeScreen:A Vision-Based Gesture Interaction System

EyeScreen is a vision-based interaction system which provides a natural gesture interface for humancomputer interaction （HCI） by tracking human fingers and recognizing gestures. Multi-view video images are captured by two cameras facing a computer screen, which can be used to detect clicking actions of a fingertip and improve the recognition rate. The system enables users to directly interact with rendered objects on the screen. Robustness of the system has been verified by extensive experiments with different user scenarios. EyeScreen can be used in many applications such as intelligent interaction and digital entertainment.

VISION-BASED MEASUREMENT OF FREE-FORM SURFACES

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Design and Evaluation of a Vision-Based UI for People with Large Cognitive-Motor Disabilities

Recovering from multiple traumatic brain injury (TBI) is a very difficult task, depending on the severity of the lesions, the affected parts of the brain and the level of damage (locomotor, cognitive or sensory). Although there are some software platforms to help these patients to recover part of the lost capacity, the variety of existing lesions and the different degree to which they affect the patient, do not allow the generalization of the appropriate treatments and tools in each case. The aim of this work is to design and evaluate a machine vision-based UI (User Interface) allowing patients with a high level of injury to interact with a computer. This UI will be a tool for the therapy they follow and a way to communicate with their environment. The interface provides a set of specific activities, developed in collaboration with the multidisciplinary team that is currently evaluating each patient, to be used as a part of the therapy they receive. The system has been successfully tested with two patients whose degree of disability prevents them from using other types of platforms.

Intelligent Path Planning of Vision-Based Mobile Robot with Fuzzy Approach

The path planning problem for intelligent mobile robots involves two main problems:the represent of task environment including obstacles and the development of a strategy to determine a collision-free route.In this paper, new approaches have been developed to solve these problems.The first problem was solved using the fuzzy system approach,which represent obstacles with a circle.The other problem was overcome through the use of a strategy selector,which chooses the best strategy between velocity control strategy and direction control strategy.

Machine Vision Based Measurement of Dynamic Contact Angles in Microchannel Flows

When characterizing flows in miniaturized channels, the determination of the dynamic contact angle is important. By measuring the dynamic contact angle, the flow properties of the flowing liquid and the effect of material properties on the flow can be characterized. A machine vision based system to measure the contact angle of front or rear menisci of a moving liquid plug is described in this article. In this research, transparent flow channels fabricated on thermoplastic polymer and sealed with an adhesive tape are used. The transparency of the channels enables image based monitoring and measurement of flow variables, including the dynamic contact angle. It is shown that the dynamic angle can be measured from a liquid flow in a channel using the image based measurement system. An image processing algorithm has been developed in a MATLAB environment. Images are taken using a CCD camera and the channels are illuminated using a custom made ring light. Two fitting methods, a circle and two parabolas, are experimented and the results are compared in the measurement of the dynamic contact angles.

A 330-500 GHz Zero-Biased Broadband Tripler Based on Terahertz Monolithic Integrated Circuits

A 330-500 GHz zero-biased broadband monolithic integrated tripler is reported. The measured results show that the maximum efficiency and the maximum output power are 2% and 194μW at 348 GHz. The saturation characteristic test shows that the output i dB compression point is about -8.5 dBm at 334 GHz and the maximum efficiency is obtained at the point, which is slightly below the 1 dB compression point. Compared with the conventional hybrid integrated circuit, a major advantage of the monolithic integrated circuit is the significant improvement of reliability and consistency. In this work, a terahertz monolithic frequency multiplier at this band is designed and fabricated.

SELECTING CLUSTER MODEL IN Sn - BASED SOLDER ALLOY DESIGN WITH DV - X_α CALCULATION METHOD

Applying calculation method in alloy design should be an important tendency due to its characters of inexpensive cost, high efficiency and prediction. DOS calculations of AuSn, AsSn and SbSn Sn- based alloys have been investigated by employing DV - Xa method, in which different cluster models were adopted to calculate electron structure.It is proved that some regulations must be taken into ac- count in order to carry out alloy design calculation successfully,which are described in this paper in detail.

Preparation and the Pore Structure of Viscose-based Activated Carbon Fibers Treated with Pretreatment Reagents

The viscose rayon was pretreated with four different pre-treatment reagents respectively, and the preparation ofactivated carbon fibers (ACF) was performed undersame carbonizing and activating conditions. The poresize distribution, the specific surface area, and the porestructure parameter of these viscose - based ACF werestudied by using quartz spring balance BET- weightmethod. The experiment result indicates that the poresize distribution and the pore structure parameter ofACF is varied by using different pretreatment reagents.

GRAPHICAL COMPUTING METHOD ON FOUR-BAR LINKAGE(1)——Inflexion Circle,Focal Axis,Ball's Point,Centering-point Curve and Circling-point Curve For Four ISPs,Coupler Curves Based on Ball's Points——

By means of programs GTMPAC based- on generalized triangle method,analysis and synthesis of mechanism design in accordance with absolutely graphicalmethod( absolutely germetrical method) are developed.In this paper,we make aspecial study about centering- point curve and circling- point curve and couplercurves based on Ball’s points.

Development of a tracking-based system for automated traffic data collection for roundabouts

Traffic data collection is essential for performance assessment, safety improvement and road planning. While automated traffic data collection for highways is relatively mature, that for roundabouts is more challenging due to more complex traffic scenes, data specifications and vehicle behavior. In this paper, the authors propose an automated traffic data collection system dedicated to roundabout scenes. The proposed system has mainly four steps of processing. First, camera calibration is performed for roundabout traffic scenes with a novel circle-based calibration algorithm. Second, the system uses enhanced Mixture of Gaussian algorithm with shaking removal for video segmentation, which can tolerate repeated camera displacements and background movements. Then, Kalman filtering, Kemel-based tracking and overlap-based opti- mization are employed to track vehicles while they are occluded and to derive the complete vehicle trajectories. The resulting vehicle trajectory of each individual vehicle gives the position, size, shape and speed of the vehicle at each time moment. Finally, a data mining algorithm is used to automatically extract the interested traffic data from the vehicle trajectories. The overall traffic data collection system has been implemented in software and runs on regular PC. The total processing time for a 3-hour video is currently 6 h. The automated traffic data collection system can significantly reduce cost and improve efficiency compared to manual data collection. The extracted traffic data have been compared to accurate manual measurements for 29 videos recorded on 29 different days, and an accuracy of more than 90% has been achieved.

Novel region-based image compression method based on spiking cortical model

To get the high compression ratio as well as the high-quality reconstructed image, an effective image compression scheme named irregular segmentation region coding based on spiking cortical model（ISRCS） is presented. This scheme is region-based and mainly focuses on two issues. Firstly, an appropriate segmentation algorithm is developed to partition an image into some irregular regions and tidy contours, where the crucial regions corresponding to objects are retained and a lot of tiny parts are eliminated. The irregular regions and contours are coded using different methods respectively in the next step. The other issue is the coding method of contours where an efficient and novel chain code is employed. This scheme tries to find a compromise between the quality of reconstructed images and the compression ratio. Some principles and experiments are conducted and the results show its higher performance compared with other compression technologies, in terms of higher quality of reconstructed images, higher compression ratio and less time consuming.

Deep Learning Framework for Classification of Emoji Based Sentiments

Recent patterns of human sentiments are highly influenced by emoji based sentiments(EBS).Social media users are widely using emoji based sentiments(EBS)in between text messages,tweets and posts.Although tiny pictures of emoji contains sufficient information to be considered for construction of classification model;but due to the wide range of dissimilar,heterogynous and complex patterns of emoji with similarmeanings(SM)have become one of the significant research areas of machine vision.This paper proposes an approach to provide meticulous assistance to social media application(SMA)users to classify the EBS sentiments.Proposed methodology consists upon three layerswhere first layer deals with data cleaning and feature selection techniques to detect dissimilar emoji patterns(DEP)with similar meanings(SM).In first sub step we input set of emoji,in second sub step every emoji has to qualify user defined threshold,in third sub step algorithm detects every emoji by considering as objects and in fourth step emoji images are cropped,after data cleaning these tiny images are saved as emoji images.In second step we build classification model by using convolutional neural networks(CNN)to explore hidden knowledge of emoji datasets.In third step we present results visualization by using confusion matrix and other estimations.This paper contributes(1)data cleaning method to detect EBS;(2)highest classification accuracy for emoji classification measured as 97.63%.

The Error Analysis Based on the Kalman Gain in a Position Predicting Algorithm of an Occluded Object

Detecting occluded objects is a crucial exercise in many spheres of application. For example in Strafing (attacking ground targets from low flying aircrafts) or vehicular tracking, continuous detection of the object even when it is occluded by another object is essential. Failing to track the occluded object may result in completely losing its location or another object to be mistakenly tracked. Both of which will result in disastrous consequences. There are various methods to handle occlusions. In a previous research which was done by the author, a novel noise filtration mechanism based on the corrector equation of the Kalman filter which can be used with greater accuracy to handle lengthy occlusions was made. In this presentation, a further analysis of the error of the algorithm will be presented. The algorithm when compared with existing algorithms under the same test conditions gives promising results.

NEW SOLID BASE DERIVED FROM AIPO4-5 SUPPORTED KF

Dispersion of KF on AlPO4-5 molecular sieve created basic sites and caused 1654 cm- band of bidentate carbonate in CO2 adsorption.After pre-treated above 823K. 5%KF/AlPO4-5exhibited basic catalytic activity in the isomerization of butenes at 273K. and the activity increasedwith the pre-treatment temperature up to 923K.