scholarly journals The impact of reducing signal acquisition specifications on neuronal spike sorting

2021 ◽  
Author(s):  
John Hermiz ◽  
Elias Joseph ◽  
Kyu Hyun Lee ◽  
Isabella A. Baldacci ◽  
Jason E. Chung ◽  
...  
Keyword(s):  
Signal Acquisition ◽  
Spike Sorting ◽  
Neuronal Spike ◽  
The Impact

Power-efficient Spike Sorting Scheme Using Analog Spiking Neural Network Classifier

2021 ◽  
Vol 17 (2) ◽  
pp. 1-29
Author(s):  
Anand Kumar Mukhopadhyay ◽  
Atul Sharma ◽  
Indrajit Chakrabarti ◽  
Arindam Basu ◽  
Mrigank Sharad
Keyword(s):  
Neural Network ◽  
Low Power ◽  
Signal Acquisition ◽  
Spiking Neural Network ◽  
Spike Sorting ◽  
Approximate Computing ◽  
Training Module ◽  
Power Efficient ◽  
On Chip ◽  
The Impact

The method to map the neural signals to the neuron from which it originates is spike sorting. A low-power spike sorting system is presented for a neural implant device. The spike sorter constitutes a two-step trainer module that is shared by the signal acquisition channel associated with multiple electrodes. A low-power Spiking Neural Network (SNN) module is responsible for assigning the spike class. The two-step shared supervised on-chip training module is presented for improved training accuracy for the SNN. Post implant, the relatively power-hungry training module can be activated conditionally based on a statistics-driven retraining algorithm that allows on the fly training and adaptation. A low-power analog implementation for the SNN classifier is proposed based on resistive crossbar memory exploiting its approximate computing nature. Owing to the direct mapping of SNN functionality using physical characteristics of devices, the analog mode implementation can achieve ∼21 × lower power than its fully digital counterpart. We also incorporate the effect of device variation in the training process to suppress the impact of inevitable inaccuracies in such resistive crossbar devices on the classification accuracy. A variation-aware, digitally calibrated analog front-end is also presented, which consumes less than ∼50 nW power and interfaces with the digital training module as well as the analog SNN spike sorting module. Hence, the proposed scheme is a low-power, variation-tolerant, adaptive, digitally trained, all-analog spike sorter device, applicable to implantable and wearable multichannel brain-machine interfaces.

scholarly journals To sort or not to sort: the impact of spike-sorting on neural decoding performance

2014 ◽  
Vol 11 (5) ◽  
pp. 056005 ◽  
Author(s):  
Sonia Todorova ◽  
Patrick Sadtler ◽  
Aaron Batista ◽  
Steven Chase ◽  
Valérie Ventura
Keyword(s):  
Spike Sorting ◽  
Neural Decoding ◽  
The Impact

scholarly journals An Experimental Study of the Feasibility of Identifying the Impact Damages of Reinforced Concrete Piers Using a Modal Frequency Method

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Xiwu Zhou ◽  
Wenchao Zhang ◽  
Yushen Gao ◽  
Guoxue Zhang ◽  
Mengdan Wen
Keyword(s):  
Reinforced Concrete ◽  
Measurement Method ◽  
Test System ◽  
Ultrasonic Waves ◽  
Test Method ◽  
Signal Acquisition ◽  
Frequency Method ◽  
Modal Frequency ◽  
The Impact ◽  
Impact Damages

In this research study, horizontal impact tests were carried out on five reduced scale pier models using China’s most advanced multifunctional ultrahigh heavy drop hammer impact test system and DHDAS dynamic signal acquisition and analysis system. Due to the fact that the traditional measurement method can only be used for local measurement damage, and the volatility is high, this paper proposes a test method for the modal frequency identification of the overall damage of reinforced concrete pier and applies the ultrasonic damage measurement method to verify the results. The tests analyzed the modal frequencies and ultrasonic velocity identifications for the purpose of evaluating the impact damages of bridge piers, as well as the relationship between them. The results showed that the modal frequencies were consistent with the ultrasonic waves in identifying and evaluating the damages to the piers. Also, the modal frequency damage factors were determined to be functions of the ultrasonic wave velocity damage factors. Therefore, the results of this study confirmed that it was feasible to characterize the impact damages of piers using a modal frequency method.

scholarly journals Ultrasonic Guided-Waves Sensors and Integrated Structural Health Monitoring Systems for Impact Detection and Localization: A Review

2021 ◽  
Author(s):  
Lorenzo Capineri ◽  
Andrea Bulletti
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Wireless Sensors ◽  
Real Life ◽  
Research Field ◽  
Time Signal ◽  
Signal Acquisition ◽  
Structural Health ◽  
Impact Detection ◽  
The Impact

In the last decade the research concerning Structural Health Monitoring (SHM) systems have continuously investigated toward autonomous systems based on sensor networks. The different functional blocks of these systems are described introducing first the basic concepts for the impact detection applications based on piezoelectric sensors for ultrasonic guided Lamb waves generated into planar structures. Then the paper will review the recent progresses of the research with focus on the integration of sensors with the electronic interface, including the embedding of sensors with the structure that is represented by the smart-skin concept. The latter benefits of the advancement in piezoelectric MEMS sensors with small footprint mounted on flexible substrates. This new layout of sensors is essential for the system design based on a network of sensors nodes with real time signal acquisition capability for impact event capture. The options of a wired or wireless sensors network are also discussed for different dimensions of the monitored structure. The multifunctional sensors capability is also a new feature discussed in the paper for sensing the environmental conditions that affect the Lamb wave signals interpretation. The power supply by environmental energy of an autonomous sensor node is another research field where large innovation is occurred and a review of energy harvesting devices is reported. The embedded signal processing capabilities in a node with IoT based wireless sensors networks, is an important fertilization between different disciplines and examples of SHM system tested in real-life application are discussed. Finally, the large capacity of data transfer of sensors networks toward large storage data archives also with low power WiFi protocols is the new frontier for exploring artificial intelligence and machine learning applied to big data and the recent research outcomes for impact detection and characterization in complex structures are reported.

scholarly journals Influence of Toothed Rail Parameters on Impact Vibration Meshing of Mountainous Self-Propelled Electric Monorail Transporter

Sensors ◽  
2020 ◽  
Vol 20 (20) ◽  
pp. 5880
Author(s):  
Yue Liu ◽  
Tiansheng Hong ◽  
Zhen Li
Keyword(s):  
Characteristic Frequency ◽  
Velocity Model ◽  
Signal Acquisition ◽  
Vibration Acceleration ◽  
Evaluation Indexes ◽  
Pressure Angle ◽  
The Stability ◽  
The Impact ◽  
Impact Vibration ◽  
Load Mass

In order to reduce the vibration of mountain self-propelled electric monorail transporters (MSEMT) caused by the impact of the meshing of roller gear with toothed rail (MRGTR), and to improve the stability and safety of monorail transporters, this paper theoretically analyzed the MRGTR mechanism of toothed monorail transporters as well as established the MSEMT displacement model and its instantaneous velocity model. The vibration signals of MSEMT with four different parameters of toothed rail were collected by the acceleration sensor and signal acquisition system. The signals were analyzed by the Hilbert envelope demodulation method to investigate the influence of toothed rail parameters on meshing impact vibration. Moreover, taking the vibration acceleration amplitude of MSEMT and the vibration attenuation time of meshing impact as evaluation indexes, a test based on the three-factor and two-level orthogonal test was engaged with factors of toothed rail pressure angle, the ratio of L—the chord length of two adjacent roller centers of a roller gear—and rack pitch p (wheel-tooth ratio) and the load mass of the MSEMT. It showed that the impact of MRGTR was the main excitation source of the vibration of MSEMT. The pressure angle and wheel-tooth ratio both have a significant impact on the smooth operation of MSEMT, the latter to a greater extent. So did the interaction between wheel-tooth ratio and load mass. The amplitude of the characteristic frequency of the MSEMT decreased with the growth of the pressure angle. When the wheel-tooth ratio was cosα, the number of the characteristic frequency was less than that when it was 1, and the amplitude became smaller too. When the pressure angle was 15, the amplitude of vibration acceleration characteristic frequency decreased as a consequence of load mass increasing. At the pressure angle of 25, the amplitude of characteristic frequency decreased with the increase of load mass if the wheel-tooth ratio was 1, and the opposite result occurs in the case when the wheel-tooth ratio was cosα. This paper provides a theoretical basis and reference for improving the impact vibration of MRGTR and optimizing the design of the toothed rail.

Neuronal Spike Sorting Based on 2-Stage RBF Networks

2008 ◽  
Author(s):  
Jianhua Dai ◽  
Xiaochun Liu ◽  
Shaomin Zhang ◽  
Huaijian Zhang ◽  
Yi Yu ◽  
...  
Keyword(s):  
Spike Sorting ◽  
Rbf Networks ◽  
Neuronal Spike

scholarly journals Boosting the signal-to-noise of low-field MRI with deep learning image reconstruction

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
N. Koonjoo ◽  
B. Zhu ◽  
G. Cody Bagnall ◽  
D. Bhutto ◽  
M. S. Rosen
Keyword(s):  
Image Reconstruction ◽  
Image Quality ◽  
Plant Root ◽  
Signal Acquisition ◽  
Neural Network Approach ◽  
Low Field ◽  
Reconstruction Methods ◽  
Reconstruction Performance ◽  
Low Field Mri ◽  
The Impact

AbstractRecent years have seen a resurgence of interest in inexpensive low magnetic field (< 0.3 T) MRI systems mainly due to advances in magnet, coil and gradient set designs. Most of these advances have focused on improving hardware and signal acquisition strategies, and far less on the use of advanced image reconstruction methods to improve attainable image quality at low field. We describe here the use of our end-to-end deep neural network approach (AUTOMAP) to improve the image quality of highly noise-corrupted low-field MRI data. We compare the performance of this approach to two additional state-of-the-art denoising pipelines. We find that AUTOMAP improves image reconstruction of data acquired on two very different low-field MRI systems: human brain data acquired at 6.5 mT, and plant root data acquired at 47 mT, demonstrating SNR gains above Fourier reconstruction by factors of 1.5- to 4.5-fold, and 3-fold, respectively. In these applications, AUTOMAP outperformed two different contemporary image-based denoising algorithms, and suppressed noise-like spike artifacts in the reconstructed images. The impact of domain-specific training corpora on the reconstruction performance is discussed. The AUTOMAP approach to image reconstruction will enable significant image quality improvements at low-field, especially in highly noise-corrupted environments.

scholarly journals Technical note: sea salt interference with black carbon quantification in snow samples using the single particle soot photometer

2021 ◽  
Author(s):  
Marco Zanatta ◽  
Andreas Herber ◽  
Zsófia Jurányi ◽  
Oliver Eppers ◽  
Johannes Schneider ◽  
...  
Keyword(s):  
Sea Ice ◽  
Black Carbon ◽  
Detection Efficiency ◽  
Sea Salt ◽  
The Arctic ◽  
Signal Acquisition ◽  
Mass Detection ◽  
High Concentration ◽  
The Impact ◽  
Snow Samples

Abstract. After deposition from the atmosphere, black carbon aerosol (BC) takes part in the snow albedo feedback contributing to modification of the Arctic radiative budget. With the initial goal of quantifying the concentration of BC in the Arctic snow and subsequent climatic impacts, snow samples were collected during the Polarstern expedition PASCAL (Polarstern cruise 106) in the sea ice covered Fram Strait in early summer 2017. The content of refractory BC (rBC) was then quantified in the laboratory of the Alfred Wegener Institute with the single particles soot photometer (SP2). We found strong correlations between both rBC mass concentration and rBC diameter with snow salinity. Therefore, we formulated the hypothesis of a salt-induced matrix effect interfering with the SP2 analysis. By replicating realistic salinity conditions, laboratory experiments indicated a dramatic six-fold reduction in observed rBC concentration with increasing salinity. In the salinity conditions tested in the present work (salt concentration below 0.4 g l−1) the impact of salt on nebulization of water droplets might be negligible. However, the SP2 mass detection efficiency systematically decreased with salinity, with the smaller rBC particles being preferentially undetected. The high concentration of suspended salt particles and the formation of thick salt coating on rBC cores might have caused problems to the SP2 analog-to-digital conversion of the signal and incandescence quenching, respectively. Changes to signal acquisition parameters and laser power of the SP2 improved the mass detection efficiency, which, nonetheless, never attained unity values. The present work provides the evidence that high concentration of sea salt undermines the quantification of rBC in snow performed with the SP2. This interference was never reported and might affect future analysis of rBC particles in snow collected, especially, over sea ice or coastal regions strongly affected by sea salt deposition.

Sign in / Sign up

Export Citation Format

Share Document