Feedback Acquisition and Reconstruction of Spectrum-Sparse Signals by Predictive Level Comparisons

In this letter, we propose a sparsity promoting feedback acquisition and reconstruction scheme for sensing, encoding and subsequent reconstruction of spectrally sparse signals. In the proposed scheme, the spectral components are estimated utilizing a sparsity-promoting, sliding-window algorithm in a feedback loop. Utilizing the estimated spectral components, a level signal is predicted and sign measurements of the prediction error are acquired. The sparsity promoting algorithm can then estimate the spectral components iteratively from the sign measurements. Unlike many batch-based Compressive Sensing (CS) algorithms, our proposed algorithm gradually estimates and follows slow changes in the sparse components utilizing a sliding-window technique. We also consider the scenario in which possible flipping errors in the sign bits propagate along iterations (due to the feedback loop) during reconstruction. We propose an iterative error correction algorithm to cope with this error propagation phenomenon considering a binary-sparse occurrence model on the error sequence. Simulation results show effective performance of the proposed scheme in comparison with the literature

EEGLAB, SIFT, NFT, BCILAB, and ERICA: New Tools for Advanced EEG Processing

We describe a set of complementary EEG data collection and processing tools recently developed at the Swartz Center for Computational Neuroscience (SCCN) that connect to and extend the EEGLAB software environment, a freely available and readily extensible processing environment running under Matlab. The new tools include (1) a new and flexible EEGLAB STUDY design facility for framing and performing statistical analyses on data from multiple subjects; (2) a neuroelectromagnetic forward head modeling toolbox (NFT) for building realistic electrical head models from available data; (3) a source information flow toolbox (SIFT) for modeling ongoing or event-related effective connectivity between cortical areas; (4) a BCILAB toolbox for building online brain-computer interface (BCI) models from available data, and (5) an experimental real-time interactive control and analysis (ERICA) environment for real-time production and coordination of interactive, multimodal experiments

An Efficient Seismic Data Acquisition Based on Compressed Sensing Architecture with Generative Adversarial Networks

© 2013 IEEE. Recently, large scale seismic data acquisition has been a critical method for scientific research and industrial production. However, due to the bottleneck on data transmission and the limitation of energy storage, it is hard to conduct large seismic data acquisition in a real-time way. So, in this paper, an efficient seismic data acquisition method, namely, compressed sensing architecture with generative adversarial networks (CSA-GAN), is proposed to tackle the two restrictions of collecting large scale seismic data. In the CSA-GAN, a data collection architecture based on compressed sensing theory is applied to reduce data traffic load of the whole system, as well as balance the data transmission. To make the compressed sensing procedure perform well in both data quality and compression ratio, a kind of generative adversarial networks is designed to learn the recovering map. According to our experiment results, a high data quality (about 30 dB) at the compression ratio of 16 is achieved by the proposed approach, which enables the system to afford 15 times more sensors and reduces the energy cost by means of data collection from N(N + 1)/2 to N2/16. These results show that the CSA-GAN can afford more sensors with the same bandwidth and consume less energy, via improving the efficiency seismic data acquisition

“What” and “when” predictions modulate auditory processing in a mutually congruent manner

Introduction: Extracting regularities from ongoing stimulus streams to form predictions is crucial for adaptive behavior. Such regularities exist in terms of the content of the stimuli and their timing, both of which are known to interactively modulate sensory processing. In real-world stimulus streams such as music, regularities can occur at multiple levels, both in terms of contents (e.g., predictions relating to individual notes vs. their more complex groups) and timing (e.g., pertaining to timing between intervals vs. the overall beat of a musical phrase). However, it is unknown whether the brain integrates predictions in a manner that is mutually congruent (e.g., if “beat” timing predictions selectively interact with “what” predictions falling on pulses which define the beat), and whether integrating predictions in different timing conditions relies on dissociable neural correlates. Methods: To address these questions, our study manipulated “what” and “when” predictions at different levels – (local) interval-defining and (global) beat-defining – within the same stimulus stream, while neural activity was recorded using electroencephalogram (EEG) in participants (N = 20) performing a repetition detection task. Results: Our results reveal that temporal predictions based on beat or interval timing modulated mismatch responses to violations of “what” predictions happening at the predicted time points, and that these modulations were shared between types of temporal predictions in terms of the spatiotemporal distribution of EEG signals. Effective connectivity analysis using dynamic causal modeling showed that the integration of “what” and “when” predictions selectively increased connectivity at relatively late cortical processing stages, between the superior temporal gyrus and the fronto-parietal network. Discussion: Taken together, these results suggest that the brain integrates different predictions with a high degree of mutual congruence, but in a shared and distributed cortical network. This finding contrasts with recent studies indicating separable mechanisms for beat-based and memory-based predictive processing

A Tutorial on Environment-Aware Communications via Channel Knowledge Map for 6G

Sixth-generation (6G) mobile communication networks are expected to have dense infrastructures, large-dimensional channels, cost-effective hardware, diversified positioning methods, and enhanced intelligence. Such trends bring both new challenges and opportunities for the practical design of 6G. On one hand, acquiring channel state information (CSI) in real time for all wireless links becomes quite challenging in 6G. On the other hand, there would be numerous data sources in 6G containing high-quality location-tagged channel data, making it possible to better learn the local wireless environment. By exploiting such new opportunities and for tackling the CSI acquisition challenge, there is a promising paradigm shift from the conventional environment-unaware communications to the new environment-aware communications based on the novel approach of channel knowledge map (CKM). This article aims to provide a comprehensive tutorial overview on environment-aware communications enabled by CKM to fully harness its benefits for 6G. First, the basic concept of CKM is presented, and a comparison of CKM with various existing channel inference techniques is discussed. Next, the main techniques for CKM construction are discussed, including both the model-free and model-assisted approaches. Furthermore, a general framework is presented for the utilization of CKM to achieve environment-aware communications, followed by some typical CKM-aided communication scenarios. Finally, important open problems in CKM research are highlighted and potential solutions are discussed to inspire future work

A study and experiment plan for digital mobile communication via satellite

The viability of mobile communications is examined within the context of a frequency division multiple access, single channel per carrier satellite system emphasizing digital techniques to serve a large population of users. The intent is to provide the mobile users with a grade of service consistant with the requirements for remote, rural (perhaps emergency) voice communications, but which approaches toll quality speech. A traffic model is derived on which to base the determination of the required maximum number of satellite channels to provide the anticipated level of service. Various voice digitalization and digital modulation schemes are reviewed along with a general link analysis of the mobile system. Demand assignment multiple access considerations and analysis tradeoffs are presented. Finally, a completed configuration is described

High-Density Diffuse Optical Tomography During Passive Movie Viewing: A Platform for Naturalistic Functional Brain Mapping

Human neuroimaging techniques enable researchers and clinicians to non-invasively study brain function across the lifespan in both healthy and clinical populations. However, functional brain imaging methods such as functional magnetic resonance imaging (fMRI) are expensive, resource-intensive, and require dedicated facilities, making these powerful imaging tools generally unavailable for assessing brain function in settings demanding open, unconstrained, and portable neuroimaging assessments. Tools such as functional near-infrared spectroscopy (fNIRS) afford greater portability and wearability, but at the expense of cortical field-of-view and spatial resolution. High-Density Diffuse Optical Tomography (HD-DOT) is an optical neuroimaging modality directly addresses the image quality limitations associated with traditional fNIRS techniques through densely overlapping optical measurements. This thesis aims to establish the feasibility of using HD-DOT in a novel application demanding exceptional portability and flexibility: mapping disrupted cortical activity in chronically malnourished children. I first motivate the need for dense optical measurements of brain tissue to achieve fMRI-comparable localization of brain function (Chapter 2). Then, I present imaging work completed in Cali, Colombia, where a cohort of chronically malnourished children were imaged using a custom HD-DOT instrument to establish feasibility of performing field-based neuroimaging in this population (Chapter 3). Finally, in order to meet the need for age appropriate imaging paradigms in this population, I develop passive movie viewing paradigms for use in optical neuroimaging, a flexible and rich stimulation paradigm that is suitable for both adults and children (Chapter 4)

Ensemble approach on enhanced compressed noise EEG data signal in wireless body area sensor network

The Wireless Body Area Sensor Network (WBASN) is used for communication among sensor nodes operating on or inside the human body in order to monitor vital body parameters and movements. One of the important applications of WBASN is patients’ healthcare monitoring of chronic diseases such as epileptic seizure. Normally, epileptic seizure data of the electroencephalograph (EEG) is captured and compressed in order to reduce its transmission time. However, at the same time, this contaminates the overall data and lowers classification accuracy. The current work also did not take into consideration that large size of collected EEG data. Consequently, EEG data is a bandwidth intensive. Hence, the main goal of this work is to design a unified compression and classification framework for delivery of EEG data in order to address its large size issue. EEG data is compressed in order to reduce its transmission time. However, at the same time, noise at the receiver side contaminates the overall data and lowers classification accuracy. Another goal is to reconstruct the compressed data and then recognize it. Therefore, a Noise Signal Combination (NSC) technique is proposed for the compression of the transmitted EEG data and enhancement of its classification accuracy at the receiving side in the presence of noise and incomplete data. The proposed framework combines compressive sensing and discrete cosine transform (DCT) in order to reduce the size of transmission data. Moreover, Gaussian noise model of the transmission channel is practically implemented to the framework. At the receiving side, the proposed NSC is designed based on weighted voting using four classification techniques. The accuracy of these techniques namely Artificial Neural Network, Naïve Bayes, k-Nearest Neighbour, and Support Victor Machine classifiers is fed to the proposed NSC. The experimental results showed that the proposed technique exceeds the conventional techniques by achieving the highest accuracy for noiseless and noisy data. Furthermore, the framework performs a significant role in reducing the size of data and classifying both noisy and noiseless data. The key contributions are the unified framework and proposed NSC, which improved accuracy of the noiseless and noisy EGG large data. The results have demonstrated the effectiveness of the proposed framework and provided several credible benefits including simplicity, and accuracy enhancement. Finally, the research improves clinical information about patients who not only suffer from epilepsy, but also neurological disorders, mental or physiological problems