Transactions of the Institute of Measurement and Control

A new general 3D object model is required in the literature of smart camera networks to facilitate future research. This paper presents a novel hierarchical and structural 3D model description which is well suited for both events detection and real-time free viewpoint surveillance. With this 3D model, sparse points are used to reconstruct objects. In addition, the state of the model is easy to track and estimate, which can be used to reduce time and computation when reconstructing the model. Further, data flow in the network is reduced to a level that smart cameras can afford. A concrete data structure of the model is described in this paper and its reconstruction method, the fusion method, is provided. Finally, experiment results show its feasibility, efficiency and effectiveness.

A vehicle with four powered caster wheels can provide much more motion flexibility in a constrained environment. However, the dynamic modelling and control of such system is challenging due to its high redundancy. This paper investigates the dynamic model and tracking control for a four-powered-caster vehicle (4-PCV) on complex terrain without any additional sensor. The torques applied to the wheels are dynamically redistributed based on the real-time conditions of the whole wheel–ground interactions so that the vehicle can track the desired trajectory when moving on different terrains. A dynamic model considering the wheel–ground interaction is first derived. Then a novel approach based on a probability scheme is proposed to identify the status of the vehicle and the wheel slip ratio by only observing the velocity feedback from motors encoders. Based on this real-time perception information, a tracking controller and a torque distribution scheme are applied to make sure that each wheel can be self-adapted to meet a complex wheel–ground condition to eliminate or reduce the probability of slippage. The effectiveness of the proposed estimation approach and the performance of the torque distribution schemes are verified by simulation.

Human upright postural control is highly related to visual information. In order to explore the influence of visual feedback on static upright postural control, postural sway of eight healthy young adults was investigated under visual feedback circumstances. In the investigation, postural feedback information was visualized by an indicator composed of a movable spot and a stationary circle, and in addition to Shannon entropy analysis, time domain and frequency domain analyses were employed to inspect postural control adjustment. The experiment results indicate that even though indicator scale changes do not induce significant postural differences in time domain and random characteristics, reduction of the visual feedback indicator scale inspires a postural power shift to higher frequencies. In addition, this reduction also induces a fall-after-rise pattern of postural energy distribution in the frequency range of 0.5–1 Hz.  

This paper describes a microfluidic-based telemedicine system for insulin detection and conveying the results digitally to physicians located off-site through the Internet. The communication infrastructure is designed to transfer the digital information from the assay site to established healthcare facilities where trained medical professionals can directly assist the detection process and provide diagnosis. The insulin detection device of the telemedicine system is an integrated polydimethysiloxane (PDMS) microfluidic device consisting of two pneumatic micropumps and one micromixer. The insulin detection protocol is based on microbeads-based double-antibody sandwich immunoassay coupled with luminal–hydrogen peroxide (H₂O₂) chemiluminescence. A photometer detects the peak value of the luminous intensity, which indicates the insulin concentration of the patient plasma sample tested. The calibration curves of the insulin detection protocol have been quantified. The insulin detection limit of the microfluidic system is 4×10⁻¹⁰ mol/l, which meets the common requirement of the current clinical studies of diabetes. Multiple immune indicators of diabetes can potentially be detected synchronously by the microfluidic system, thus providing physicians with integrative results necessary for accurate diagnosis via the Internet. The combination of microfluidic devices and telemedicine strategy offers new opportunities for diabetes care and screening, especially in rural areas where patients must travel long distances to physicians for healthcare information that might be obtained more cost effectively by local, less-trained personnel.

Chinese sign language has been proved as an effective communication tool for deaf people. In this paper, we present a novel translation system, which can capture human gestures through micro-inertial measurement units (IMUs) and translate the gestures into specific meanings accordingly. Each micro-IMU consists of a 3D accelerometer and gyroscope. A micro-controller and ZigBee network were used to acquire data simultaneously and wirelessly. Ten types of basic Chinese sign language movements including what, how, work, today, happy, please, book, body, clothes and and were collected and stored to form a motion-sensing database. A discrete cosine transform (DCT) was performed to extract the effective features from the original data, while a hidden Markov model (HMM) was used to train the database in order to form an HMM classifier. Testing samples were used to test the HMM classifier. Different sign languages were recognized through the HMM classifier and subsequent translation processes were performed. Experimental results showed that the correct recognition rate ranges from 95% to 100% for the 10 sign language movements, and the overall correction rate is 98%. With more micro-electro-mechanical system (MEMS) sensing motes adding to the interpretation system, the performance will be enhanced.

This paper presents a systematic trajectory generation method for a bipedal robot walking on slopes. A suitable offline walking pattern based on an inverted pendulum model is designed and the planning is parameterized by step lengths, step period and other walking parameters. Meanwhile, a slight unevenness of a slope can cause serious instability for bipedal walking robots. Therefore, this paper also proposes an online control algorithm for a bipedal robot even walking on the unevenness slope, and the robot can adapt to the floor conditions. The control algorithm includes a landing time controller, landing direction controller, zero moment point (ZMP) regulation controller and attitude correction controller. During the process, accurate attitude information for these controllers is achieved through an adaptive filtering method and the ZMP position is measured through force sensing register sensors, which are attached to the robot’s feet. Finally, the experiment is carried out on a SCUT-I humanoid robot. The result proves that the method described in this paper can successfully control a robot walking on slopes.

This paper focuses on the automatic generation of forward kinematics of a kind of modular reconfigurable robot. Based on the modularized division of robots and the communication between the host and the modules, a configuration recognition method is proposed. By using the graph theory, the method of topological analysis is proposed, and the assembly incidence matrix (AIM) and path matrix are derived. Subsequently, based on the results of topological analysis and the definition of module frames, the initial poses and twists of a robot are obtained. To deal with the multi-chain robots, the entries of the path matrix are employed to enable dyads to appear or not to appear in the kinematic equations. Then, the forward kinematics of the multi-chain robot is derived. An illustrative example and an experiment are presented. The results show that the method is valid and suitable for both single-open-chain robots and multi-chain robots.

The gap sensor plays an important role for a electro-magnetic levitation system, which is a critical component of high-speed maglev trains. An artificial neural network is a promising area in the development of intelligent sensors. In this paper, a radial basis function (RBF) neural network modelling approach is introduced for the compensation of the non-contact inductive gap sensor of the high-speed maglev train. As an inverse model compensator, the designed RBF-based model is connected in series to the output terminal of the gap sensor. The network is trained by using a gradient descent learning algorithm with momentum. This scheme could estimate accurately the correct air-gap distance in a wide range of temperatures. The simulation studies of this model show that it can provide a compensated gap value with an error of less than ±0.4 mm at any temperature from 20° to 80°C. In particulr, the maximum estimation error can be reduced to ±0.1 mm when the working gap varies from 8 to 12 mm. The experimental results indicate that the compensated gap signal could meet the requirements of the levitation control system.

Outlier detection plays an important role in intelligent cyber systems, especially for fault-tolerant and adaptive ones. Traditional algorithms always need to evaluate distances or densities, which are very time-consuming. Considering the increasingly urgent demand for real-time application, during the past few years, various novel algorithms have been proposed. They are much faster, but less stable and less accurate. To cope with these problems, based on the core idea of ordinal optimization and the ‘few and different’ characteristics of outliers, by introducing the concept of outlier probability, we propose this ordinal outlier detection algorithm (OOD), which extracts outliers in terms of the order of being isolated in a recursive uniform data space partitioning process. It does not need any distance or density evaluation, and the complexity is reduced to O(n). Experiments show that the CPU time of OOD increases linearly with linearly growing data sets. Furthermore, compared with the recent iForest algorithm, OOD is about 30 times faster, with a 20–30% improvement in accuracy and in particular is much more stable. OOD also has good scalability, so it works well in high-dimensional data sets, which have a huge number of instances and irrelevant attributes.

A new algorithm named the likelihood-based iteration square-root cubature Kalman filter (LISRCKF) is provided in this study. The LISRCKF inherits the virtues of the square-root cubature Kalman filter (SRCKF), which uses the cubature rule-based numerical integration method to calculate the mean and square root of covariance for the non-linear random function. The LISRCKF involves the use of the iterative measurement update and the use of the latest measurement, and the iteration termination criterion based on maximum likelihood is introduced in the measurement update. The LISRCKF algorithm is applied to the state estimation for re-entry ballistic target with unknown ballistic coefficient. Its performance is compared against that of the unscented Kalman filter and SRCKF. Moreover, the suitable choice of iteration number is studied; iteration number 5 is the most appropriate for the LISRCKF algorithm. Simulation results indicate that the LISRCKF algorithm has the features of short run time and fast convergence rate; the advantage in robustness is also demonstrated through the numerical simulation, and it is an effective state estimation method.