We present a high spatiotemporal resolution system for neuroimaging using functional near-infrared spectroscopy (fNIRS). The system is configured as bundled optodes with a single photodiode (PD) and 128 dual-wavelength LEDs in a module. This system is developed using a modular approach where a single module can cover approximately 7 cm × 7 cm, while multiple modules can be used to a broader area. The system has the capacity to measure concentration changes of oxyhemoglobin (HbO) and deoxyhemoglobin (HbR) at different brain depths ranging from 2 cm to 3.5 cm. The system also provides the superficial layer information by measuring the short-separation channels. The short-separation channels allow removal of noise and enhancement of signals. The drive circuit of LEDs is carefully designed to switch the light with appropriate intensity, which provides a stable reception for each channel. MOSFET based switching is implemented that allows sharp current switching for high-speed data acquisition. The system can display the acquired HbO and HbR signals as well as activation maps in real-time on a lab-developed Windows-based software. The hardware connects to the software using Wi-Fi. Phantom model with known optical properties and a human subject were used for testing the functionality and efficacy of the device. A complete 128 channel fNIRS sample was recorded in 25 ms. The phantom results showed reduced signal intensity when the channel separation was increased that provides the HbO and HbR. The activation was seen using HbO in the human subject while performing hand tapping task.
In this paper, the effect of various channel selection strategies on the initial dip phase of the hemodynamic response (HR) using functional near-infrared spectroscopy (fNIRS) is investigated. The strategies using channel averaging, channel averaging over a local region, t-value-based channel selection, baseline correction, and vector phase analysis are examined. For t-value-based channel selection, three gamma functions are used to model the initial dip, the main HR, and the undershoot in generating the designed HR function. The linear discriminant analysis based classification accuracy is used as performance evaluation criteria. fNIRS signals are obtained from the left motor cortex during righthand thumb and little finger tapping tasks. In classifying two finger tapping tasks, signal mean and minimum value during 0~2.5 sec, as features of initial dip, are used. The results show that the active channel selected using t-value and vector phase analysis yielded the highest averaged classification accuracy. It is also found that the initial dip in the HR disappears in case of averaging overall channels. The results demonstrated the importance of the channel selection in improving the classification accuracy for fNIRS-based brain-computer interface applications. Furthermore, the use of three gamma functions can also be useful for fNIRS brain imaging for detecting the initial dip in the HR.
The reduction of trial-to-trial variability (TTV) in task-evoked functional near-infrared spectroscopy signals by considering the correlated low-frequency spontaneous fluctuations that account for the resting-state functional connectivity in the brain is investigated. A resting-state session followed by a task-state session of a right hand finger-tapping task has been performed on five subjects. Significant ipsilateral and bilateral resting-state functional connectivity has been detected at the subjects’ motor cortex using the seed correlation method. The correlation coefficients obtained during the resting-state are used to reduce the TTV in the signals measured during the task sessions. The results suggest that correlated spontaneous low-frequency fluctuations contribute significantly to the TTV in the task evoked fNIRS signals.
The control objectives in this paper are to move the gantry of a container crane to its target position and to suppress the transverse vibration of the payload. The crane system is modeled as an axially moving nonlinear string, in which control inputs are applied at both ends, through the gantry and the payload. The dynamics of the moving string are derived using Hamilton's principle. The Lyapunov function method is used in deriving a boundary control law, in which the Lyapunov function candidate is introduced from the total mechanical energy of the system. The performance of the proposed control law is compared with other two control algorithms available in the literature. Experimental results are given.
In this paper, a real-time reactive mechanism in the hybrid deliberate/reactive control architecture was proposed. The real-time reactive layer consists of resources, behaviors, an action coordinator, and a controller. Each resource offers an independent sensor information. To improve real-time characteristics, individual behaviors and the action coordinator were designed to fulfill the realtimeness in executing each component in RTAI (Real-Time Application Interface for Linux). The effectiveness and the real-time characteristics of the proposed reactive mechanism were verified by computer simulations of preplanned scenarios.
Proc. SPIE. 3692, Acquisition, Tracking, and Pointing XIII
KEYWORDS: Target detection, Detection and tracking algorithms, Sensors, Error analysis, Monte Carlo methods, Design for manufacturing, Sensing systems, Electronic filtering, Filtering (signal processing), Data fusion
The IMM estimator is known as a suboptimal hybrid filter that has been shown to be one of the most cost-effective hybrid state estimation schemes. The value of hybrid models for tracking algorithm is that the occurrence of target maneuvers can be explicitly included in the kinematic equations through regime jumps. However, the model probabilities of IMM filter trend to be slowly adapted from the non-maneuver mode to maneuver mode or from maneuver to non-maneuver, although sudden maneuver can take place in the true system. This is why the model probability is dependent on the past model probabilities. In order to track a suddenly and highly maneuvering target, the technique which combines IMM filter with error monitoring and recovery technique of perception nit is proposed in this paper. The perception net, as a structural representation of the sensing capabilities of system, is formed by the interconnection of logical senor with three types of modules: feature transformation module, data fusion module (DFM), and constraint satisfaction module. Observing the output and input of DFM, we can detect an error of input data. Error monitoring and recovery technique based on this function makes it possible to detect and identity errors, and to calibrate possible biases involved in sensed data and extracted features. Both detecting maneuver and compensating the estimated state can be achieved by employing the properly implemented error monitoring and recovery technique reduces the maximum values of estimation errors when maneuvering starts and finishes, and shows higher tracking performance especially for a highly maneuvering target. Its effectiveness is demonstrated through Monte-Carlo simulations.