2,988 research outputs found
Communication channel analysis and real time compressed sensing for high density neural recording devices
Next generation neural recording and Brain-
Machine Interface (BMI) devices call for high density or distributed
systems with more than 1000 recording sites. As the
recording site density grows, the device generates data on the
scale of several hundred megabits per second (Mbps). Transmitting
such large amounts of data induces significant power
consumption and heat dissipation for the implanted electronics.
Facing these constraints, efficient on-chip compression techniques
become essential to the reduction of implanted systems power
consumption. This paper analyzes the communication channel
constraints for high density neural recording devices. This paper
then quantifies the improvement on communication channel
using efficient on-chip compression methods. Finally, This paper
describes a Compressed Sensing (CS) based system that can
reduce the data rate by > 10x times while using power on
the order of a few hundred nW per recording channel
Jointly Sparse Support Recovery via Deep Auto-encoder with Applications in MIMO-based Grant-Free Random Access for mMTC
In this paper, a data-driven approach is proposed to jointly design the
common sensing (measurement) matrix and jointly support recovery method for
complex signals, using a standard deep auto-encoder for real numbers. The
auto-encoder in the proposed approach includes an encoder that mimics the noisy
linear measurement process for jointly sparse signals with a common sensing
matrix, and a decoder that approximately performs jointly sparse support
recovery based on the empirical covariance matrix of noisy linear measurements.
The proposed approach can effectively utilize the feature of common support and
properties of sparsity patterns to achieve high recovery accuracy, and has
significantly shorter computation time than existing methods. We also study an
application example, i.e., device activity detection in Multiple-Input
Multiple-Output (MIMO)-based grant-free random access for massive machine type
communications (mMTC). The numerical results show that the proposed approach
can provide pilot sequences and device activity detection with better detection
accuracy and substantially shorter computation time than well-known recovery
methods.Comment: 5 pages, 8 figures, to be publised in IEEE SPAWC 2020. arXiv admin
note: text overlap with arXiv:2002.0262
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
Bioelectronic Sensor Nodes for Internet of Bodies
Energy-efficient sensing with Physically-secure communication for bio-sensors
on, around and within the Human Body is a major area of research today for
development of low-cost healthcare, enabling continuous monitoring and/or
secure, perpetual operation. These devices, when used as a network of nodes
form the Internet of Bodies (IoB), which poses certain challenges including
stringent resource constraints (power/area/computation/memory), simultaneous
sensing and communication, and security vulnerabilities as evidenced by the DHS
and FDA advisories. One other major challenge is to find an efficient on-body
energy harvesting method to support the sensing, communication, and security
sub-modules. Due to the limitations in the harvested amount of energy, we
require reduction of energy consumed per unit information, making the use of
in-sensor analytics/processing imperative. In this paper, we review the
challenges and opportunities in low-power sensing, processing and
communication, with possible powering modalities for future bio-sensor nodes.
Specifically, we analyze, compare and contrast (a) different sensing mechanisms
such as voltage/current domain vs time-domain, (b) low-power, secure
communication modalities including wireless techniques and human-body
communication, and (c) different powering techniques for both wearable devices
and implants.Comment: 30 pages, 5 Figures. This is a pre-print version of the article which
has been accepted for Publication in Volume 25 of the Annual Review of
Biomedical Engineering (2023). Only Personal Use is Permitte
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