Number Of Data Segments Research Articles

Empirical validation of task-related component analysis reformulation for computational complexity reduction

The task-related component analysis (TRCA) is a data-driven method for extracting reproducible components across multiple data segments from multivariate data. TRCA has been proven effective in enhancing the signal-to-noise ratio of neuroimaging data in previous studies. However, its original form requires a computational cost of O(N2) to compute the sum of cross-covariance matrices, indicating that the computational time increases rapidly as the number of data segments N increases. This study aims to empirically validate that the reformulated form of TRCA can reduce its computational complexity. This study estimated that the reformulation can reduce the computational complexity from O(N2) to O(N) without distorting its outputs by reducing the number of matrix multiplications in computing the sum of cross-covariance matrices. A series of computer simulations using computer-generated synthetic, functional near-infrared spectroscopy (fNIRS), and electroencephalogram (EEG) data were conducted to verify the theoretical computation complexity and consistency of the outputs between the original and reformulated TRCA. The computation reduction was validated through an experimental comparison of the original and reformulated TRCA with synthetic and real data of various sizes (e.g., data segments, dimensions, and lengths). In addition, the two algorithms output almost exactly identical results with only floating-point errors. The reformulation would considerably benefit practical applications, especially when applying the TRCA to large-scale computations. The code is available at https://github.com/mnakanishi/TRCA-SSVEP.

Collecting all data continuously in wireless sensor networks with a mobile base station

In this paper, we present a novel coding scheme called separate coding SC for collecting all data continuously in wireless sensor networks with a mobile base station mBS. By separately encoding a certain number of data segments in a combined segment, and storing the combined segments in the corresponding buffers of each sensor node, the SC scheme provides an efficient storage method to collect all data segments. By randomly querying a small subset of sensor nodes, the mBS can reconstruct all the original data segments with high probability. We prove that the success ratio of SC-based data collection is very close to 100%. The number of sensor nodes that should be queried by the mBS can be reduced with a few additional storage space in each sensor node. The performance evaluation has been conducted through computer simulations. It further demonstrates the feasibility and superiority of the proposed SC scheme.

Distributed Separate Coding for Continuous Data Collection in Wireless Sensor Networks

In this article, we present a novel distributed separate coding (DSC) scheme for continuous data collection in wireless sensor networks with a mobile base station (mBS). By separately encoding a certain number of data segments in a combined segment and doing decoding-free data replacement in the buffers of each sensor node, the DSC scheme is shown as an efficient method for continuously collecting data segments with a high success ratio. The proposed DSC scheme has a salient feature: with a minimum buffer size 2 in each sensor node, by querying any m −1 sensor nodes, the mBS can reconstruct the m latest data segments with high probability, where m is the number of latest data segments in a time interval t in which n ( t ) ( m ≤ n ( t )) data segments are generated. The necessary storage space in each sensor node can be adjusted by changing the number of sensor nodes queried by the mBS. Furthermore, the transmission cost for data submission to the mBS can be reduced with some additional storage space in each sensor node. The comprehensive performance evaluation has been conducted through computer simulation. It is shown that the proposed DSC scheme outperforms the existing scheme significantly.

Analysis of brain connectivity changes after propofol injection by generalized partial directed coherence

In this paper we present a method for the analysis of multichannel EEG by using Generalized Partial Directed Coherence (gPDC) to extract cortical connectivity changes under anesthesia. 15 channel EEG data were recorded from female subjects undergoing general anesthesia for gynecological surgery. Multivariate Autoregressive (MAR) modeling was applied to multichannel, bipolar EEG segments of awake and anesthetized states. gPDCs were calculated using the derived MAR model and averaged through EEG α frequency band (8–14 Hz) and through a number of data segments. The gPDC calculation was performed for three different sets of bipolar EEG channel pairs each of which mainly represent a specific scalp area. To derive consistency levels of gPDC values, surrogate analysis is also performed. Using paired t-test for 12 patients, we extracted significant gPDC changes between awake and anesthetized stages for each set. Analysis revealed that during transition from awake to anesthetized stage, gPDCs of central to parietal directions were suppressed whereas gPDCs of parietal to central directions were increased. The results indicate that the proposed gPDC analysis method can be used to track the changes in brain connectivity and hence to estimate the depth of anesthesia.

Base Station Placement for Effective Data Dissemination in Sensor Networks

Reprogramming sensor nodes is important for managing sensor networks. The latest reprogramming protocols use radio communication to distribute software data. In multi-base station sensor networks, the placement of the base stations affects several wireless reprogramming performance metrics. We developed a method for placing base stations, and we evaluated the features of software dissemination for multi-base station sensor networks. Simulations showed that the placement and number of base stations and the number of data segments were the key parameters in software dissemination.

Improving the detection of evoked responses to periodic stimulation by using multiple coherence—application to EEG during photic stimulation

The coherence between the stimulation signal and the electroencephalogram (EEG) has been used in the detection of evoked responses. However the detector’s performance depends on both the signal-to-noise ratio (SNR) of the responses and the number of data segments ( M) used in coherence estimation. In this work, a technique for detecting evoked responses was developed based on the extension to the multivariate case of this coherence. Thus, instead of using the EEG collected at a unique region, the estimation is proposed using two EEG derivations. As for the univariate case, this multiple coherence is independent of the stimulation signal. In addition, considering equal SNR in both signals, the detection rate with this multiple coherence is always greater than that one using only one signal. This was verified in Monte Carlo simulations, which also showed that a superior performance is still expected in practical situations, when a smaller SNR is found in the second signal. The results with EEG from 12 normal subjects during photic stimulation confirm this better performance. Since the proposed technique allows a higher detection rate without the need of increasing M, it permits evoked responses to be detected faster, which is very useful during monitored surgeries.

Near-field performance of passive coherence processing sonars

Recently, passive sonar systems using two or more stationary receivers have been used for the detection and localization of moving acoustic targets. In far-field configurations, where the receivers are remotely located from the source, the use of the magnitude-squared coherence function (MSCF) has proved attractive. In this paper near-field configurations are considered, where the interreceiver spacing is not appreciably greater than the distance from the source. The concept of an extended MSCF (EMSCF) is introduced to take into account the relative Doppler shift and time delay between the signals at two receivers. Asymptotic expressions are derived for the EMSCF as the number of data segments becomes large. The effect on the EMSCF of noise in the received signals is analyzed by obtaining approximate expressions for the mean and variance of the density function of the EMSCF. An example is presented to illustrate the use of these expressions in the evaluation of the performance of passive sonar detectors.