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In this paper, a single antenna interference cancellation (SAIC) by means of sequential importance sampling (SIS) is studied. The contribution of the proposed algorithm is that the maximum a posteriori (MAP) estimates of the transmitted co-channel symbols is recursively computed without explicit channel information, and the joint posterior distribution of the channels is derived. The Symbol error...
Detecting changes in remote sensing images taken at different times is challenging when images' data come from different sensors. The performance of change detection algorithms based on radiometric values alone is not satisfactory and need the fusion of other features. Local similarity measures such as Mutual Information, Kullback-Leibler Divergence, and Cluster Reward Algorithm can be used for enhancing...
Location information has become a critical requirement for many applications in wireless sensor networks. Conventional localization requires dense anchors with known positions or high transmit power in sparse networks to reach successful localization, which is not suitable for low-cost and low-power sensors. Cooperative localization is a promising solution for wireless sensors' localization, in which...
Unsupervised gait training of patients using a robotic trainer requires an individual adaption of the modelbased control for each patient as well as a feedback of the patient's performance. For these adaption and feedback tasks, estimation of the kinematic states, the friction states, and the patient-dependent parameters of the model is necessary. In this contribution, a combined estimation approach...
In this paper we describe a control framework that integrates tactile and force sensing to regulate the physical interaction of an anthropomorphic robotic arm with the external environment. In particular, we exploit tactile sensors distributed on the robot fingers and a 6-axis force/torque sensor place at the bottom of the arm, just below the shoulder. Due to their different mounting locations and...
Both time synchronization and localization are critical issues for wireless sensor networks (WNSs) especially for Underwater Sensor Networks (USNs). In this paper, we have proposed an ameliorated joint synchronization and localization (AJSL) algorithm for 3D USNs using iterative weighted least square (IWLS) estimator. This algorithm takes advantage of projection technique with measurable depth information...
We consider an asynchronous sensor network where target sensors aim to locate themselves unambiguously. Timing information is a vital requirement for time-based localization. Joint localization and synchronization estimation can improve position accuracy and reduce both communication overhead and power cost. In this paper, a novel distributed cooperative joint localization and synchronization algorithm...
Functional effective muscle (FEM) theory can derive characteristics of limbs theoretically from detailed muscular strength. However, it is difficult to measure each muscular strength directly from outside the body. This paper proposes a new method for the estimation of muscular strength from maximum output force distribution at the end-effector of a limb. The proposed estimation method based on FEM...
For the presence of the multi-path propagation, it is difficult for us to estimate the elevation of a low-height target. In this paper, a new method for the joint estimation of the target height and the reflecting surface height is proposed based on multichannel digital array radar. According to the model of multi-path propagation in low angle tracking, the joint estimation is obtained by minimizing...
A sparse-training-sequence-aided orthogonal frequency division multiplexing system is proposed to enable channel estimation and data detection without being affected by carrier frequency offset (CFO) effect. With the aid of the sparse training sequence, an approximate maximum likelihood estimation on CFO effect and channel response is developed to retrieve the CFO and channel response information...
We report the development of a human whole-body pose estimation scheme with application to rider-bicycle interactions. The estimation scheme is built on the fusion of measurements of a monocular camera on the bicycle and a set of small wearable gyroscopes attached to the rider's upper- and lower-limb and the trunk. A single feature point is collocated with each wearable gyroscope and also on the segment...
Tracking whole-body human pose in physical human-machine interactions such as bicycling is challenging because of highly-dimensional human motions and lack of inexpensive, effective motion sensors in outdoor environment. In this paper, we present a computational scheme to estimate the whole-body pose in human-machine interaction with application to the rider-bicycle system. The estimation scheme is...
To successfully manipulate in unknown environments, a robot must be able to perceive degrees of freedom of objects in its environment. Based on the resulting kinematic model and joint configurations, the robot is able to select and adapt actions, recognize their successful completion and detect failure. We present an RGB-D-based online algorithm for the interactive perception of articulated objects...
This paper focuses on a possibility of application of a method to simplification of robotic arm systems by removal joint encoders. The method which has been proposed achieves precise control of robotic arms using visual feedback with camera calibration errors. In this paper, the method is improved for an encoderless robotic arm system to ease several difficulties to use encoders. Encoders are fundamental...
We present a neural dynamics architecture for grasping that integrates perceptual processes of scene exploration, object selection and classification, and grasp pose estimation with motor processes such as planning and controlling reach and grasp movements. Inspired by theories of human embodied cognition, the entire architecture is essentially one big dynamical system from which discrete events such...
Accurately identifying human's intent of motion from electromyography (EMG) signals is the key to implement EMG-based HRI (Human-Robot Interface) systems. Human's intent of motion includes motion modes and continuous movement variables. In this paper, a hybrid EMG-to-motion model is constructed by combining a classification model and a regression model. Based on a proper division for joints, the classification...
In linear decentralized estimation, several nodes concurrently aim to estimate the state of a common phenomenon by means of local measurements and data exchanges. In this contribution, an efficient algorithm for consistent estimation of linear systems in sensor networks is derived. The main theorems generalize Covariance Intersection by means of an explicit consideration of individual noise terms...
Surface electromyographic (EMG) signals have often been used in estimating upper and lower limb dynamics and kinematics for the purpose of controlling robotic devices such as robot prosthesis and finger exoskeletons. However, in estimating multiple and a high number of degrees-of-freedom (DOF) kinematics from EMG, output DOFs are usually estimated independently. In this study, we estimate finger joint...
During physiological gait, humans continuously modulate their knee stiffness, depending on the demands of the activity and the terrain. A similar functionality could be provided by modern actuators in transfemoral prosthesis. However, quantitative data on how knee stiffness is modulated during physiological gait is still missing. This is likely due to the experimental difficulties associated with...
The accuracy of the estimation of time-delay and channel coefficients in Orthogonal frequency division multiplexing (OFDM) communication systems can be improved by reducing the variability of channel coefficients, i.e. reducing channel covariance and increasing channel mean for a given power. First, we prove that the effect of channel variability between different OFDM symbols cannot be directly captured...
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