246 research outputs found
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
Quantum dynamic response-based NV-diamond magnetometry: Robustness to decoherence and applications in motion detection of magnetic nanoparticles
We propose a novel quantum sensing protocol that leverages the dynamical
response of physical observables to quenches in quantum systems. Specifically,
we use the nitrogen-vacancy (NV) color center in diamond to realize both scalar
and vector magnetometry via quantum response. Furthermore, we suggest a method
for detecting the motion of magnetic nanoparticles, which is challenging with
conventional interference-based sensors. To achieve this, we derive the closed
exact form of the Berry curvature corresponding to NV centers and design
quenching protocols to extract the Berry curvature via dynamical response. By
constructing and solving non-linear equations, the magnetic field and
instantaneous motion velocity of the magnetic nanoparticle can be deduced. We
investigate the feasibility of our sensing scheme in the presence of
decoherence and show through numerical simulations that it is robust to
decoherence. Intriguingly, we have observed that a vanishing nuclear spin
polarization in diamond actually benefits our dynamic sensing scheme, which
stands in contrast to conventional Ramsey-based schemes. In comparison to
Ramsey-based sensing schemes, our proposed scheme can sense an arbitrary
time-dependent magnetic field, as long as its time dependence is nearly
adiabatic.Comment: 12 pages, 2 figure
EIE: Efficient Inference Engine on Compressed Deep Neural Network
State-of-the-art deep neural networks (DNNs) have hundreds of millions of
connections and are both computationally and memory intensive, making them
difficult to deploy on embedded systems with limited hardware resources and
power budgets. While custom hardware helps the computation, fetching weights
from DRAM is two orders of magnitude more expensive than ALU operations, and
dominates the required power.
Previously proposed 'Deep Compression' makes it possible to fit large DNNs
(AlexNet and VGGNet) fully in on-chip SRAM. This compression is achieved by
pruning the redundant connections and having multiple connections share the
same weight. We propose an energy efficient inference engine (EIE) that
performs inference on this compressed network model and accelerates the
resulting sparse matrix-vector multiplication with weight sharing. Going from
DRAM to SRAM gives EIE 120x energy saving; Exploiting sparsity saves 10x;
Weight sharing gives 8x; Skipping zero activations from ReLU saves another 3x.
Evaluated on nine DNN benchmarks, EIE is 189x and 13x faster when compared to
CPU and GPU implementations of the same DNN without compression. EIE has a
processing power of 102GOPS/s working directly on a compressed network,
corresponding to 3TOPS/s on an uncompressed network, and processes FC layers of
AlexNet at 1.88x10^4 frames/sec with a power dissipation of only 600mW. It is
24,000x and 3,400x more energy efficient than a CPU and GPU respectively.
Compared with DaDianNao, EIE has 2.9x, 19x and 3x better throughput, energy
efficiency and area efficiency.Comment: External Links: TheNextPlatform: http://goo.gl/f7qX0L ; O'Reilly:
https://goo.gl/Id1HNT ; Hacker News: https://goo.gl/KM72SV ; Embedded-vision:
http://goo.gl/joQNg8 ; Talk at NVIDIA GTC'16: http://goo.gl/6wJYvn ; Talk at
Embedded Vision Summit: https://goo.gl/7abFNe ; Talk at Stanford University:
https://goo.gl/6lwuer. Published as a conference paper in ISCA 201
A hydrogel beads based platform for single-cell phenotypic analysis and digital molecular detection
Microfluidic platforms integrating phenotyping and genotyping approaches have the potential to advance the understanding of single cell genotype-to-phenotype correlations. These correlations can play a key role in tackling antibiotic heteroresistance, cancer cell heterogeneity, and other related fundamental problems. Herein, we report a novel platform that enables both high-throughput digital molecular detection and single-cell phenotypic analysis, utilizing nanoliter-sized biocompatible polyethylene glycol hydrogel beads produced by a convenient and disposable centrifugal droplet generation device. The hydrogel beads have been demonstrated enhanced thermal stability, and achieved uncompromised efficiencies in digital polymerase chain reaction, digital loop-mediated isothermal amplification, and single cell phenotyping. The crosslinked hydrogel network highlights the prospective linkage of various subsequent molecular analyses to address the genotypic differences between cellular subpopulations exhibiting distinct phenotypes. Our platform shows great potential for applications in clinical practice and medical research, and promises new perspectives in mechanism elucidation of environment-evolution interaction and other basic research areas
Graph Neural Network-Enhanced Expectation Propagation Algorithm for MIMO Turbo Receivers
Deep neural networks (NNs) are considered a powerful tool for balancing the
performance and complexity of multiple-input multiple-output (MIMO) receivers
due to their accurate feature extraction, high parallelism, and excellent
inference ability. Graph NNs (GNNs) have recently demonstrated outstanding
capability in learning enhanced message passing rules and have shown success in
overcoming the drawback of inaccurate Gaussian approximation of expectation
propagation (EP)-based MIMO detectors. However, the application of the
GNN-enhanced EP detector to MIMO turbo receivers is underexplored and
non-trivial due to the requirement of extrinsic information for iterative
processing. This paper proposes a GNN-enhanced EP algorithm for MIMO turbo
receivers, which realizes the turbo principle of generating extrinsic
information from the MIMO detector through a specially designed training
procedure. Additionally, an edge pruning strategy is designed to eliminate
redundant connections in the original fully connected model of the GNN
utilizing the correlation information inherently from the EP algorithm. Edge
pruning reduces the computational cost dramatically and enables the network to
focus more attention on the weights that are vital for performance. Simulation
results and complexity analysis indicate that the proposed MIMO turbo receiver
outperforms the EP turbo approaches by over 1 dB at the bit error rate of
, exhibits performance equivalent to state-of-the-art receivers with
2.5 times shorter running time, and adapts to various scenarios.Comment: 15 pages, 12 figures, 2 tables. This paper has been accepted for
publication by the IEEE Transactions on Signal Processing. Copyright may be
transferred without notice, after which this version may no longer be
accessibl
Gradient-Based Markov Chain Monte Carlo for MIMO Detection
Accurately detecting symbols transmitted over multiple-input multiple-output
(MIMO) wireless channels is crucial in realizing the benefits of MIMO
techniques. However, optimal MIMO detection is associated with a complexity
that grows exponentially with the MIMO dimensions and quickly becomes
impractical. Recently, stochastic sampling-based Bayesian inference techniques,
such as Markov chain Monte Carlo (MCMC), have been combined with the gradient
descent (GD) method to provide a promising framework for MIMO detection. In
this work, we propose to efficiently approach optimal detection by exploring
the discrete search space via MCMC random walk accelerated by Nesterov's
gradient method. Nesterov's GD guides MCMC to make efficient searches without
the computationally expensive matrix inversion and line search. Our proposed
method operates using multiple GDs per random walk, achieving sufficient
descent towards important regions of the search space before adding random
perturbations, guaranteeing high sampling efficiency. To provide augmented
exploration, extra samples are derived through the trajectory of Nesterov's GD
by simple operations, effectively supplementing the sample list for statistical
inference and boosting the overall MIMO detection performance. Furthermore, we
design an early stopping tactic to terminate unnecessary further searches,
remarkably reducing the complexity. Simulation results and complexity analysis
reveal that the proposed method achieves near-optimal performance in both
uncoded and coded MIMO systems, adapts to realistic channel models, and scales
well to large MIMO dimensions.Comment: This work has been submitted to the IEEE for possible publication.
Copyright may be transferred without notice, after which this version may no
longer be accessibl
Comparison of the BCI Performance between the Semitransparent Face Pattern and the Traditional Face Pattern
Brain-computer interface (BCI) systems allow users to communicate with the external world by recognizing the brain activity without the assistance of the peripheral motor nervous system. P300-based BCI is one of the most common used BCI systems that can obtain high classification accuracy and information transfer rate (ITR). Face stimuli can result in large event-related potentials and improve the performance of P300-based BCI. However, previous studies on face stimuli focused mainly on the effect of various face types (i.e., face expression, face familiarity, and multifaces) on the BCI performance. Studies on the influence of face transparency differences are scarce. Therefore, we investigated the effect of semitransparent face pattern (STF-P) (the subject could see the target character when the stimuli were flashed) and traditional face pattern (F-P) (the subject could not see the target character when the stimuli were flashed) on the BCI performance from the transparency perspective. Results showed that STF-P obtained significantly higher classification accuracy and ITR than those of F-P (p < 0.05)
Comparison of the ERP-Based BCI Performance Among Chromatic (RGB) Semitransparent Face Patterns
Objective: Previous studies have shown that combing with color properties may be used as part of the display presented to BCI users in order to improve performance. Build on this, we explored the effects of combinations of face stimuli with three primary colors (RGB) on BCI performance which is assessed by classification accuracy and information transfer rate (ITR). Furthermore, we analyzed the waveforms of three patterns. Methods: We compared three patterns in which semitransparent face is overlaid three primary colors as stimuli: red semitransparent face (RSF), green semitransparent face (GSF), and blue semitransparent face (BSF). Bayesian linear discriminant analysis (BLDA) was used to construct the individual classifier model. In addition, a Repeated-measures ANOVA (RM-ANOVA) and Bonferroni correction were chosen for statistical analysis. Results: The results indicated that the RSF pattern achieved the highest online averaged accuracy with 93.89%, followed by the GSF pattern with 87.78%, while the lowest performance was caused by the BSF pattern with an accuracy of 81.39%. Furthermore, significant differences in classification accuracy and ITR were found between RSF and GSF (p < 0.05) and between RSF and BSF patterns (p < 0.05). Conclusion: The semitransparent faces colored red (RSF) pattern yielded the best performance of the three patterns. The proposed patterns based on ERP-BCI system have a clinically significant impact by increasing communication speed and accuracy of the P300-speller for patients with severe motor impairment
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