Communication Theoretic Data Analytics

Widespread use of the Internet and social networks invokes the generation of big data, which is proving to be useful in a number of applications. To deal with explosively growing amounts of data, data analytics has emerged as a critical technology related to computing, signal processing, and information networking. In this paper, a formalism is considered in which data is modeled as a generalized social network and communication theory and information theory are thereby extended to data analytics. First, the creation of an equalizer to optimize information transfer between two data variables is considered, and financial data is used to demonstrate the advantages. Then, an information coupling approach based on information geometry is applied for dimensionality reduction, with a pattern recognition example to illustrate the effectiveness. These initial trials suggest the potential of communication theoretic data analytics for a wide range of applications.Comment: Published in IEEE Journal on Selected Areas in Communications, Jan. 201

Frequency Recognition in SSVEP-based BCI using Multiset Canonical Correlation Analysis

Canonical correlation analysis (CCA) has been one of the most popular methods for frequency recognition in steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs). Despite its efficiency, a potential problem is that using pre-constructed sine-cosine waves as the required reference signals in the CCA method often does not result in the optimal recognition accuracy due to their lack of features from the real EEG data. To address this problem, this study proposes a novel method based on multiset canonical correlation analysis (MsetCCA) to optimize the reference signals used in the CCA method for SSVEP frequency recognition. The MsetCCA method learns multiple linear transforms that implement joint spatial filtering to maximize the overall correlation among canonical variates, and hence extracts SSVEP common features from multiple sets of EEG data recorded at the same stimulus frequency. The optimized reference signals are formed by combination of the common features and completely based on training data. Experimental study with EEG data from ten healthy subjects demonstrates that the MsetCCA method improves the recognition accuracy of SSVEP frequency in comparison with the CCA method and other two competing methods (multiway CCA (MwayCCA) and phase constrained CCA (PCCA)), especially for a small number of channels and a short time window length. The superiority indicates that the proposed MsetCCA method is a new promising candidate for frequency recognition in SSVEP-based BCIs

Development of a Post-Processing Algorithm for Accurate Human Skull Profile Extraction via Ultrasonic Phased Arrays

Ultrasound Imaging has been favored by clinicians for its safety, affordability, accessibility, and speed compared to other imaging modalities. However, the trade-offs to these benefits are a relatively lower image quality and interpretability, which can be addressed by, for example, post-processing methods. One particularly difficult imaging case is associated with the presence of a barrier, such as a human skull, with significantly different acoustical properties than the brain tissue as the target medium. Some methods were proposed in the literature to account for this structure if the skull\u27s geometry is known. Measuring the skull\u27s geometry is therefore an important task that requires attention. In this work, a new edge detection method for accurate human skull profile extraction via post-processing of ultrasonic A-Scans is introduced. This method, referred to as the Selective Echo Extraction algorithm, SEE, processes each A-Scan separately and determines the outermost and innermost boundaries of the skull by means of adaptive filtering. The method can also be used to determine the average attenuation coefficient of the skull. When applied to simulated B-Mode images of the skull profile, promising results were obtained. The profiles obtained from the proposed process in simulations were found to be within 0.15 λ ± 0.11 λ or 0.09 ± 0.07 mm from the actual profiles. Experiments were also performed to test SEE on skull mimicking phantoms with major acoustical properties similar to those of the actual human skull. With experimental data, the profiles obtained with the proposed process were within 0.32 λ ± 0.25 λ or 0.19 ± 0.15 mm from the actual profile