Classification of EEG signal using EACA based approach at SSVEP-BCI

The brain-computer-interfaces (BCI) can also be referred towards a mindmachine interface that can provide a non-muscular communication channel in between the computer device and human brain. To measure the brain activity, electroencephalography (EEG) has been widely utilized in the applications of BCI to work system in real-time. It has been analyzed that the identification probability performed with other methodologies do not provide optimal classification accuracy. Therefore, it is required to focus on the process of feature extraction to achieve maximum classification accuracy. In this paper, a novel process of data-driven spatial has been proposed to improve the detection of steady state visually evoked potentials (SSVEPs) at BCI. Here, EACA has been proposed, which can develop the reproducibility of SSVEP across many trails. Further this can be utilized to improve the SSVEP from a noisy data signal by eliminating the activities of EEG background. In the simulation process, the SSVEP dataset recorded from given 11 subjects are considered. To validate the performance, the state-of-art method is considered to compare with the EDCA based proposed approach.

The Gaitprint: Identifying Individuals by Their Running Style

Recognizing the characteristics of a well-developed running style is a central issue in athletic sub-disciplines. The development of portable micro-electro-mechanical-system (MEMS) sensors within the last decades has made it possible to accurately quantify movements. This paper introduces an analysis method, based on limit-cycle attractors, to identify subjects by their specific running style. The movement data of 30 athletes were collected over 20 min. in three running sessions to create an individual gaitprint. A recognition algorithm was applied to identify each single individual as compared to other participants. The analyses resulted in a detection rate of 99% with a false identification probability of 0.28%, which demonstrates a very sensitive method for the recognition of athletes based solely on their running style. Further, it can be seen that these differentiations can be described as individual modifications of a general running pattern inherent in all participants. These findings open new perspectives for the assessment of running style, motion in general, and a person’s identification, in, for example, the growing e-sports movement.

A Preamble Detecting Algorithm of MOD-5 Interrogating Signal

Propose a preamble detecting Algorithm in the processing of MOD-5 signal, after researching in the feature of MOD-5 interrogating preamble pulse. This paper resume the MOD-5 working principle, demonstrate the algorithm flow and simulation parameters. An analysis of the simulation result had been done at last. This algorithm can provide the ability of noise suppression and precise timing. On that basis, it can be the foundation in IFF (Identification Friend of Foe) signal processing with the purpose of increasing successful identification probability.

Multi-Feature Extraction and Fusion for the Underwater Moving Targets Classification

The underwater moving targets classfication are important for the underwater surveillance system.This paper presents the classification algrithms based on the multi-feature fusion, including the target echo highlight features,temporal features from the assocition images interframe,and the moving features after tracking.The principal compoment analysis was used to reduce the feature dimension and the k-means algorithm was used for classification. At last,the experiment results of the classification between the divers and underwater vehicles are given, which show that the multifeature fusion can improve the classification performance.And the PCA algorithm can reduce the feature dimension without lower the identification probability.

Observational Selection in Spectroscopic Binary Eccentricities

The discovery-and-identification probability for different shaped orbits of spectroscopic binary stars is estimated. The eccentricity distribution observed in the sample of ~1000 binaries with known orbits and appearing as strongly peaked toward e=0 is corrected for observational selection effects. The resulting e-distribution seems to be flat for e in the range ~0.05-0.6 with some excess of circular (or almost circular) orbits and a deficiency of orbits with e ≲ 0.6.