Water detection is important for machine vision applications such as visual inspection and robot motion planning. In this paper, we propose an approach to per-pixel water detection on unknown surfaces with a hyperspectral image. Our proposed method is based on the water spectral characteristics: water is transparent for visible light but translucent/opaque for near-infrared light and therefore the apparent near-infrared spectral reflectance of a surface is smaller than the original one when water is present on it. Specifically, we use a linear combination of a small number of basis vector to approximate the spectral reflectance and estimate the original near-infrared reflectance from the visible reflectance (which does not depend on the presence or absence of water) to detect water. We conducted a number of experiments using real images and show that our method, which estimates near-infrared spectral reflectance based on the visible spectral reflectance, has better performance than existing techniques.

In this paper, we consider the relationship between human preference and brain activity, especially pulse wave information using NIRS. First of all, we extracted the information of on pulse wave from the Hb changes signal of NIRS. By using the FFT to the Hb signals, we found out the 2-nd peak of power spectrum that is implying the frequency information of the pulse wave. The frequency deviation of 2-nd peak may have some information about the change of brain activity, it is associated with the human preference for viewing the significant image content.

Functional near-infrared spectroscopy (fNIRS) is a noninvasive neuroimaging technique, suitable for measurement during motor learning. However, effects of contamination by systemic artifacts derived from the scalp layer on learning-related fNIRS signals remain unclear. Here we used fNIRS to measure activity of sensorimotor regions while participants performed a visuomotor task. The comparison of results using a general linear model with and without systemic artifact removal shows that systemic artifact removal can improve detection of learning-related activity in sensorimotor regions, suggesting the importance of removal of systemic artifacts on learning-related cerebral activity.

We have developed a portable NIRS-based optical BCI system that features a non-invasive, facile probe attachment and does not require muscle movement to control the target devices. The system consists of a 2-channel probe, a signal-processing unit, and an infrared-emission device, which measures the blood volume change in the participant's prefrontal cortex in a real time. We use the threshold logic as a switching technology, which transmits a control signal to a target device when the electrical waveforms exceed the pre-defined threshold. Eight healthy volunteers participated in the experiments and they could change the television channel or control the movement of a toy robot with average switching times of 11.5 ± 5.3 s and the hit rate was 83.3%. These trials suggest that this system provides a novel communication aid for people with motor disabilities.

Iris recognition is an important biometric method for personal identification. The accuracy of an iris recognition system highly depends on the success of an iris segmentation step. In this paper, a robust and accurate iris segmentation algorithm for closed-up NIR eye images is developed. The proposed method addressed problems of different characteristics of iris databases using local image properties. A precise pupil boundary is located with an adaptive thresholding combined with a gradient-based refinement approach. A new criteria, called a local signal-to-noise ratio (LSNR) of an edge map of an eye image is proposed for localization of the iris's outer boundary. The boundary is modeled with a weighted circular integral of LSNR optimization technique. The proposed method is experimented with multiple iris databases. The obtained results demonstrated that the proposed iris segmentation method is robust and desirable. The proposed method accurately segments iris region, excluding eyelids, eyelashes and light reflections against multiple iris databases without parameter tunings. The proposed iris segmentation method reduced false negative rate of the iris recognition system by half, compared to results obtained using Masek's method.

We have developed a prototype optical brain-computer interface (BCI) system that can be used by an operator to manipulate external, electrically controlled equipment. Our optical BCI uses near-infrared spectroscopy and functions as a compact, practical, unrestrictive, non-invasive brain-switch. The optical BCI system measured spatiotemporal changes in the hemoglobin concentrations in the blood flow of a subject's prefrontal cortex at 22 measurement points. An exponential moving average (EMA) filter was applied to the data, and then their weighted sum with a task-related parameter derived from a pretest is utilized for time-indicated control (GO-STOP) of an external object. In experiments using untrained subjects, the system achieved control patterns within an accuracy of 6 sec for more than 80% control.

It is well known that Orthogonal Frequency Division Multiplexing (OFDM) scheme is robust to frequency selective fading in wireless channels. However, once delayed signals beyond a guard interval of an OFDM symbol are introduced in a channel with large delay spread, inter-symbol interference causes a severe degradation in the transmission performance. In this paper, we propose a novel pre-Fast Fourier Transform (FFT) OFDM adaptive antenna array, which requires only one FFT processor at a receiver, for suppressing such delayed signals. We analytically derive the optimum weights for the beamformer based on the Maximum Signal-to-Noise-and-Interference power Ratio (SNIR) and the Minimum Mean Square Error (MMSE) criteria, respectively. Computer simulation results show its good performance even in a channel where Directions of Arrival (DoAs) of arriving waves are randomly determined.

We describe a DS-CDMA adaptive modulation system in which high-rate-data for moving pictures and LANs is transmitted to a high-speed traveling mobile terminal in the down-link. The transmission data rate is constant by changing the data-modulation level and the number of multiplex channels. We use computer simulation to evaluate the performance of the system using a RAKE receiver in a multipath-channel environment. For fdTslot 0.08, which is fading maximum Doppler frequency fd normalized by slot time Tslot, the following results are obtained. The average bit error rate (BER) of BER 1 10-3 necessary to ensure quality of high-rate-data transmission for moving pictures and LANs without error correction is attainable at low symbol-to-noise power ratio of ES/N0 14 dB and channel-use rate lower than 65%. The cell capacity of 17.2% is about 1.4 times that of the conventional system. Also, fdTslot=0.08 corresponds to the traveling speed of about 250 km/h at a carrier frequency of 8 GHz. Thus, the system enables high-rate-data and high-quality transmission needed for the moving pictures and LANs at mobile terminals with a traveling speed higher than 100 km/h at high carrier frequencies of the microwave band.