Estimating distance to an object on the horizon using wave motion

This paper describes an optimal guidance policy for a vehicle to reach a relative position to a target, using information from a single flxed camera. Applying an extended Kalman flltering method, both of a velocity and a position of the vehicle relative to the target, can be estimated. However, estimates of a distance between the vehicle and the target are much worse than those of the other states. Therefore, in this paper, an optimal guidance policy is introduced that can reach the destination while maximizing the predicted accuracy of the range estimation. By limiting vehicle motion to a two dimensionals, the exact solution for control inputs for this optimization problem can be obtained. Simulation results show that the resulting optimal guidance policy gives far more accurate range estimation than a simple linear guidance policy. The automation of unmanned aerial vehicles(UAVs), has progressively developed in recent years. The development of sensors, such as GPS, made a large contribution. In most cases, UAV automated ∞ight control has been achieved by using multi- sensor fusion to estimate vehicle states accurately. However, because this becomes a complex and expensive system and not suitable for small unreusable UAVs, there is a need to make an autonomous ∞ight system which is simpler and less expensive. As seen in nature birds or insects, vision information can be almost exclusively utilized in an autonomous ∞ight system. This has the potential to improve system reliability, performance, and cost. Inspired from this idea, this paper introduces a method to navigate and guide a vehicle to reach a target using vision information from a single camera flxed to the vehicle. In this approach, an extended Kalman fllter(EKF) is utilized to estimate relative velocity and position. A single camera provides measurements of target horizontal and vertical position in its image, with noise. By applying an EKF to these two measurements, estimates of the relative position and velocity are obtained. The EKF estimation performance depends on camera motion, and the estimation will be improved by an appropriate trajectory generation. 1 In the case of the vehicle approaching straight toward the target, the velocity and position in the camera image plane can be estimated with relatively good accuracy. However, the estimation of distance between the vehicle(or camera) and the target includes large errors. If we use these poor range estimates to control a vehicle performing station-keeping with the target, a dangerous overshoot may occur. The reason for this large estimation error is that the vehicle does not necessary have enough lateral motion to estimate this depth. It is well known that the accuracy of range estimation depends on camera translating motion, and the best translation for range estimation is a motion parallel to its image plane. 2 Therefore, if we let the vehicle ∞y to the target with meandering path, we can obtain the more accurate range estimates and avoid the overshoot. Through the EKF, the variance of estimation errors for each state is available. To maximize the estimation accuracy, one should minimize the variance. In other words, to obtain the optimal ∞ight path to the relative position to the target, we have to set the performance index in order to minimize the variance of the estimation error of range and solve the resulting optimization problem subjected to a known camera motion. In this paper, we show that we can solve this problem analytically. Then, comparing simulation results of

The United States National Oceanic and Atmospheric Administration (NOAA) Center for Operational Oceanographic Products and Services (CO-OPS) is responsible for developing and maintaining the National Water Level Observation Network (NWLON), which consists of over 200 long-term observatories located across U.S. coasts, including the Great Lakes and U. S. island territories. CO-OPS continues to analyze state-of-the-art and emerging technologies to identify potential improvements in data quality and operating efficiency and to maintain core expertise for authorized missions. The international ocean observing community has recognized that microwave radar range sensors offer many potential benefits for long-term sea level monitoring. The most notable advantage of such sensors is the ability to measure water level remotely and from above the sea surface. Over the past 3.5 years, CO-OPS has conducted a series of extensive laboratory and long-term field tests with several different microwave radar range sensors to determine their suitability for use as water level sensors in NWLON and other CO-OPS measurements systems. Intermediate results from CO-OPS' extensive test efforts have been previously reported at various conferences and through several reports over the last three years. This paper provides a summary update on field test analysis conducted to date and reports a significant milestone to which most recent results have led: CO-OPS has recommended limited acceptance of radar water level sensors for use in its network of coastal observatories, and a transition of the new technology to operational applications is currently underway. In addition to a summary of most recent field test results, highlights from CO-OPS' plan to transition the microwave radar water level sensors to its network of operational coastal observatories will be presented, along with progress on initial operational installations. A summary of continued field testing to better understand microwave radar sensor performance capability and limitations in open ocean, large wave environments will also be presented.

Computer-vision-based methods provide one general approach for obstacle detection and range estimation for pilot assistance during low-altitude flight. Results obtained using helicopter flight data with a feature-based range estimation algorithm are presented. A method for recursively estimating range using a Kalman filter with a monocular sequence of images and knowledge of the camera's motion is described. The helicopter flight experiment and one of four resulting datasets are briefly discussed. The performance of the range estimation algorithm is examined by comparing the range estimates with true range measurements collected during the flight experiment. >

Laser scanner has several bons when compared with video camera. It does not record real world videos except scanned points. As a result, processing of data becomes faster and easier. Over and above, it takes away the problem of private life conservation. This paper proposes a new and competent computer vision based approach for detecting and tracking targets (e.g., pedestrians and vehicles) from laser scanned datasets. Laser scanned data points from each scan have been deemed as a video frame. Blobs are extracted and then computer vision techniques (e.g., Kalman filter, Hungarian algorithms, and etc.) are applied to recognize and track the kind of targets. Scanned datasets, collected from two kinds of laser scanners, were used to conduct experiments. Full trajectories of pedestrians, vehicles, and noises were resulted in three dimensional spaces. Experimental results give evidence of the efficacy of our proposed framework.

Visual perception for walking machines needs to handle more degrees of freedom than for wheeled robots. For humanoid, four- or six-legged robots, camera motion is 6D instead of 3D or planar motion. Classical 3D reconstruction methods cannot be applied directly, because explicit sensor motion is needed. In this paper, we propose an algorithm for 3D reconstruction of an unstructured environment using motion-free uncalibrated single camera. Computer vision techniques are employed to obtain an incremental geometrical reconstruction of the environment, therefore using vision as a sensor for robot control tasks like navigation, obstacle avoidance, manipulation, tracking, etc. and 3D model acquisition. The main contribution is that the offline 3D reconstruction problem is considered as a point trajectory search through the video stream. The algorithm takes into account the temporal aspect of the sequence of images in order to have an analytical expression of the geometrical locus of the point trajectories through the sequence of images. The approach is a generalization of the Desargues theorem applied to multiple views taken from nearby viewpoints. Experiments on both synthetic and real image sequences show the simplicity and efficiency of the proposed method. This method provides an alternative technical solution easy to use, flexible in the context of robotic applications and can significantly improve the 3D estimation accuracy.

We propose a framework for automatically generating multiple clips suitable for video editing by simulating pan-tilt-zoom camera movements within the frame of a single static camera. Assuming important actors and objects can be localized using computer vision techniques, our method requires only minimal user input to define the subject matter of each sub-clip. The composition of each sub-clip is automatically computed in a novel L1-norm optimization framework. Our approach encodes several common cinematographic practices into a single convex cost function minimization problem, resulting in aesthetically pleasing sub-clips which can easily be edited together using off-the-shelf multi-clip video editing software. We demonstrate our approach on five video sequences of a live theatre performance by generating multiple synchronized subclips for each sequence.

Constant power load issue in DC/DC multi-converter systems: past studies and recent trends

During the laser cladding process, thermal stresses are induced because of the high-energy input, high temperature gradient, fast cooling rate, and inconsistency of the clad-substrate material. The induced thermal stresses not only increase the crack tendency, but also influence the mechanical performance of the deposited layer. In this study, a three dimensional (3D) uncoupled thermo-mechanical finite element (FE) model was established to simulate the stress evolution of laser cladding of cobalt-based coatings on mild steel A36. The temperature field was simulated first and then used as transient thermal loading to simulate the stress evolution. Stress distributions for three cases: single track on a flat substrate, double-track on a flat substrate, and double-track on a cylindrical substrate, were investigated in detail. To check the accuracy of the simulation results, validation experiments were carried out using an 8-kW high-power direct diode laser. The thermocouples were used to monitor the temperature cycles at several marked points. The cross-sections of single and double tracks on a flat substrate obtained experimentally were compared with the simulation results. The residual stress on the clad was experimentally determined by an X-ray diffraction machine. The experimentally obtained data showed a significant consistency with the prediction results.

In recent years, the expansion of marine oil and gas transport has led to the increase of oil spill accidents. Both accidents occurring in deep sea and coastal regions can bring huge financial losses. As the development of ocean model, oil spill model has been proposed and became a valid countermeasure to simulate the process of transport, diffusion and transformation of the surface oil in seawater. This paper presents an oil spill model with a novel cross-platform (Windows/Linux) architecture. This model can not only compute oil concentration mathematically and numerically, but also can draw oil spill concentration maps with pseudo color, by using computer visualization technique. The tendency of transport, diffusion and transformation of oil could be illustrated in chronological order by the concentration maps. Furthermore, an oil spill simulation case has been accomplished, and the simulation results is discussed in this paper. Results showed that this model could simulate oil slick motions effectively and had friendly user interface.

The kinematics parameters measurement of the target by the environmental perception radar sensor is the basic guarantee for the safe and efficient driving of intelligent vehicle. 24GHz millimeter-wave radar is distinct from 77GHz millimeter-wave radar in the speed, range and angle measurement performance due to the different working frequency band and bandwidth among the existed mainstream commercial automotive radar sensors. In order to evaluate the speed and range measurement performance of 24GHz and 77GHz millimeter-wave radar sensors, this paper develops and builds a set of kinematic parameters calibration facility for automotive millimeter-wave radars based on virtual instrument technology, and verifies the feasibility and simulation accuracy of the simulated calibration facility through the speed and range simulated calibration results of the 24GHz and 77GHz millimeter-wave radar samples. The results of simulated calibration and uncertainty evaluation comparison show that the speed measurement repeatability and accuracy of the 77GHz millimeter-wave radar sample are superior to that of the 24GHz millimeter-wave radar sample.

In this tutorial paper, we systematically present the fundamental operating principles and analytical details of the discrete Fourier transform based signal processing techniques for the TDM MIMO FMCW millimeter-wave (mmWave) automotive radars. The mmWave radars provide a key sensing capability to support safety features of the conventional and autonomous vehicles. Automotive radar sensors are used to detect presence and location of the objects of interest to derive comprehensive and accurate knowledge of road conditions and surrounding environments. Automotive radars are subjected to an increasing demand to provide high-resolution measurements in the range-Doppler-azimuth-elevation domains. Therefore, the current state-of-the-art automotive radars commonly employ MIMO technologies, resulting in a large block of multidimensional data to process in real time through a long chain of signal processing algorithms. Detailed coverage on the fundamental radar signal processing techniques are scattered in a large body of literature for classical radar systems. Currently, there is no tutorial available that covers the technical details of the latest TDM MIMO FMCW radar technology, making it extremely hard for new researchers in the field of automotive mmWave radars. This paper contains sufficient technical details to serve as a tutorial. It can help lay a solid analytical foundation to facilitate exploration and development of advanced radar signal processing techniques for automotive applications. The algorithmic details and analytical results presented in this paper can be readily applied to both real-time implementation and post-processing. Simulation and experimental results are presented in this paper to validate analytical derivations and to demonstrate the capabilities of the TDM MIMO radar sensor in practical implementations.

This paper describes a new radar sensor architecture comprising an array of transceiver modules. Target applications of the sensor are automotive driver assistance systems. In conjunction with a monolithic integration of each transceiver, the concept offers the possibility for a cost-effective realization of digital-beamforming radar sensors at millimeter-wave frequencies. A modulation sequence is investigated based on simultaneously transmitted frequency-modulated continuous-wave signals, which are separated by frequency multiplexing. Appropriate signal processing for the estimation of range, speed, and azimuth angle in multiple object situations is presented. Experimental results with an eight-channel radar sensor in the 76-77-GHz frequency band are presented, which demonstrate the feasibility of the proposed architecture and show the performance of the modulation sequence and signal processing.

Recent years have seen a rise in the use of various machine learning techniques in computer vision, particularly in posing feature-based human action recognition which includes convolutional neural networks (CNN) and recurrent neural network (RNN). CNN-based methods are useful in recognizing human actions for combined motions (i.e., standing up, hand shaking, walking). However, in case of uncertainty of camera motion, occlusion, and multiple people, CNN suppresses important feature information and is not efficient enough to recognize variations for human action. Besides, RNN with long short-term memory (LSTM) requires more computational power to retain memories to classify human actions. This research proposes an extended framework based on capsule network using silhouette pose features to recognize human actions. Proposed extended framework achieved high accuracy of 95.64% which is higher than previous research methodology. Extensive experimental validation of the proposed extended framework reveals efficiency which is expected to contribute significantly in action recognition research.

It is extremely time consuming for researchers looking for particular events of interest to manually search in the video database. Therefore, there is enormous scope in research in the field of automatic extraction of key frames from underwater video sequences. Analysis of underwater video poses many challenges to existing techniques in computer vision including camera movement, turbidity, uneven background, non uniform lighting, moving objects in the underwater and inaccessibility of environment. The main aim of this paper is to (i) describe about the existing methods for key frame extraction, (ii) emphasize the key challenges in underwater environment, (iii) propose a new method to extract key frames which suits for underwater environment, (iv) perform the automatic detection and characterization of events of interest in underwater videos.

Real-time image stabilization is particular useful for portable video cameras to compensate the vibrations caused by the photographer. Although several stabilization algorithms have been proposed, very few of them consider the practical implementation issues in a camera platform but only simulate the algorithms in computers. In this paper, we propose a robust real-time algorithm to estimate global motion vector (GMV) and stabilize the video frames by fully utilizing the available computational engine in a typical camera system-on-a-chip (SoC) platform. The resulting video is much stable than the one without applying stabilization, and the speed is fast enough for real-time video recording applications