Self-navigated 4D cartesian imaging of periodic motion in the body trunk using partial k-space compressed sensing

2016 ◽  
Vol 78 (2) ◽  
pp. 632-644 ◽  
Author(s):  
Thomas Küstner ◽  
Christian Würslin ◽  
Martin Schwartz ◽  
Petros Martirosian ◽  
Sergios Gatidis ◽  
...  
Keyword(s):  
Compressed Sensing ◽  
Periodic Motion ◽  
The Body ◽  
Cartesian Imaging

scholarly journals Deep learning can accelerate and quantify simulated localized correlated spectroscopy

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Zohaib Iqbal ◽  
Dan Nguyen ◽  
Michael Albert Thomas ◽  
Steve Jiang
Keyword(s):  
Deep Learning ◽  
Compressed Sensing ◽  
Mean Squared Error ◽  
Learning Model ◽  
The Body ◽  
Resonance Spectroscopy ◽  
Atomic Structures ◽  
Squared Error ◽  
Deep Learning Model ◽  
Cosy Spectra

AbstractNuclear magnetic resonance spectroscopy (MRS) allows for the determination of atomic structures and concentrations of different chemicals in a biochemical sample of interest. MRS is used in vivo clinically to aid in the diagnosis of several pathologies that affect metabolic pathways in the body. Typically, this experiment produces a one dimensional (1D) 1H spectrum containing several peaks that are well associated with biochemicals, or metabolites. However, since many of these peaks overlap, distinguishing chemicals with similar atomic structures becomes much more challenging. One technique capable of overcoming this issue is the localized correlated spectroscopy (L-COSY) experiment, which acquires a second spectral dimension and spreads overlapping signal across this second dimension. Unfortunately, the acquisition of a two dimensional (2D) spectroscopy experiment is extremely time consuming. Furthermore, quantitation of a 2D spectrum is more complex. Recently, artificial intelligence has emerged in the field of medicine as a powerful force capable of diagnosing disease, aiding in treatment, and even predicting treatment outcome. In this study, we utilize deep learning to: (1) accelerate the L-COSY experiment and (2) quantify L-COSY spectra. All training and testing samples were produced using simulated metabolite spectra for chemicals found in the human body. We demonstrate that our deep learning model greatly outperforms compressed sensing based reconstruction of L-COSY spectra at higher acceleration factors. Specifically, at four-fold acceleration, our method has less than 5% normalized mean squared error, whereas compressed sensing yields 20% normalized mean squared error. We also show that at low SNR (25% noise compared to maximum signal), our deep learning model has less than 8% normalized mean squared error for quantitation of L-COSY spectra. These pilot simulation results appear promising and may help improve the efficiency and accuracy of L-COSY experiments in the future.

scholarly journals Effective Low-Power Wearable Wireless Surface EMG Sensor Design Based on Analog-Compressed Sensing

2021 ◽  
Author(s):  
Mohammadreza Balouchestani ◽  
Sridhar Krishnan
Keyword(s):  
Compressed Sensing ◽  
Bayesian Learning ◽  
Mean Squared Error ◽  
Sampling Rate ◽  
Computation Time ◽  
Reconstruction Algorithm ◽  
Surface Emg ◽  
Healthcare Systems ◽  
The Body ◽  
Reconstruction Process

Surface Electromyography (sEMG) is a non-invasive measurement process that does not involve tools and instruments to break the skin or physically enter the body to investigate and evaluate the muscular activities produced by skeletal muscles. The main drawbacks of existing sEMG systems are: (1) they are not able to provide real-time monitoring; (2) they suffer from long processing time and low speed; (3) they are not effective for wireless healthcare systems because they consume huge power. In this work, we present an analog-based Compressed Sensing (CS) architecture, which consists of three novel algorithms for design and implementation of wearable wireless sEMG bio-sensor. At the transmitter side, two new algorithms are presented in order to apply the analog-CS theory before Analog to Digital Converter (ADC). At the receiver side, a robust reconstruction algorithm based on a combination of ℓ1-ℓ1-optimization and Block Sparse Bayesian Learning (BSBL) framework is presented to reconstruct the original bio-signals from the compressed bio-signals. The proposed architecture allows reducing the sampling rate to 25% of Nyquist Rate (NR). In addition, the proposed architecture reduces the power consumption to 40%, Percentage Residual Difference (PRD) to 24%, Root Mean Squared Error (RMSE) to 2%, and the computation time from 22 s to 9.01 s, which provide good background for establishing wearable wireless healthcare systems. The proposed architecture achieves robust performance in low Signal-to-Noise Ratio (SNR) for the reconstruction process.

scholarly journals Trotting Motion of the Quadruped Model with Two Spinal Joints and Its Dynamics Features

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Zhang Li ◽  
Yuegang Tan
Keyword(s):  
Periodic Motion ◽  
Solution Space ◽  
The Body ◽  
Quadruped Robots ◽  
Hybrid Controller ◽  
Hybrid Dynamics ◽  
Stable Periodic ◽  
Reaction Forces ◽  
Motion Performance ◽  
Passive Model

The spine plays important roles in the quadruped locomotion. To investigate the effects of the spine on the quadruped trotting motion, firstly, a sagittal passive model is proposed which contains four massless springy legs and two passive spinal joints. To generate the trotting gait of the model, the multibody hybrid dynamics model is established based on the defined events. The combination of optimization tools is used to find the suitable solution space in which the model can maintain a periodic motion. It reveals that the quadruped trotting motion results from the coordinated features of the spine and the legs. By comparing the model with the rigid body, it is proven that the spinal joints can reduce the effect of the ground reaction forces on the body in a special velocity range. Then, a hybrid controller whose objective is to maintain the kinematic coordination between the spinal joints is applied and it replaces the passive spinal joints, and the results prove that it can make the model achieve a stable periodic motion. Finally, the prototype of the quadruped robot with two spinal joints based on the model is established and its trotting motion is achieved successfully. The experiment results also indicate the compliant effect of the spine on the motion performance. Consequently, the effects of the spine at trotting gait are helpful to guide the development of the quadruped robots.

scholarly journals Effective Low-Power Wearable Wireless Surface EMG Sensor Design Based on Analog-Compressed Sensing

2021 ◽  
Author(s):  
Mohammadreza Balouchestani ◽  
Sridhar Krishnan
Keyword(s):  
Compressed Sensing ◽  
Bayesian Learning ◽  
Mean Squared Error ◽  
Sampling Rate ◽  
Computation Time ◽  
Reconstruction Algorithm ◽  
Surface Emg ◽  
Healthcare Systems ◽  
The Body ◽  
Reconstruction Process

Surface Electromyography (sEMG) is a non-invasive measurement process that does not involve tools and instruments to break the skin or physically enter the body to investigate and evaluate the muscular activities produced by skeletal muscles. The main drawbacks of existing sEMG systems are: (1) they are not able to provide real-time monitoring; (2) they suffer from long processing time and low speed; (3) they are not effective for wireless healthcare systems because they consume huge power. In this work, we present an analog-based Compressed Sensing (CS) architecture, which consists of three novel algorithms for design and implementation of wearable wireless sEMG bio-sensor. At the transmitter side, two new algorithms are presented in order to apply the analog-CS theory before Analog to Digital Converter (ADC). At the receiver side, a robust reconstruction algorithm based on a combination of ℓ1-ℓ1-optimization and Block Sparse Bayesian Learning (BSBL) framework is presented to reconstruct the original bio-signals from the compressed bio-signals. The proposed architecture allows reducing the sampling rate to 25% of Nyquist Rate (NR). In addition, the proposed architecture reduces the power consumption to 40%, Percentage Residual Difference (PRD) to 24%, Root Mean Squared Error (RMSE) to 2%, and the computation time from 22 s to 9.01 s, which provide good background for establishing wearable wireless healthcare systems. The proposed architecture achieves robust performance in low Signal-to-Noise Ratio (SNR) for the reconstruction process.

Study on dynamic characteristics of bio-inspired vibration isolation platform

2021 ◽  
pp. 107754632199361
Author(s):  
Yong Song ◽  
Chong Zhang ◽  
Zhanlong Li ◽  
Yue Li ◽  
Jinyi Lian ◽  
...  
Keyword(s):  
Dynamic Characteristics ◽  
Periodic Motion ◽  
Vibration Isolation ◽  
Structural Parameters ◽  
Body Movement ◽  
Nonlinear Energy Sink ◽  
The Body ◽  
Object A ◽  
Time Frequency ◽  
Resonant Phenomenon

Inspired by the body movement of the kangaroo, a multi-degree-of-freedom vibration isolation platform containing three units, that is, a protected object, a nonlinear energy sink, and an X-shaped structure, has been modeled, and the differential equations of the system have been given in the form of uniform relative coordinates. Furthermore, the displacement transmissibility analysis and numerical calculation are supported by the method of harmonic balance and Runge–Kutta algorithm, which shows that (a) there are nonlinear behaviors and resonant phenomenon in the time–frequency response and (b) quasi-periodic motion may be a predictor of periodic steady-state response or strong resonance, and the displacement evolution before quasi-periodic motion may be used to distinguish the two phenomena. In addition, based on the numerical method, the system energy changes in a selected frequency are discussed. Finally, the correctness of the theoretical analysis is verified by simulation data in Adams. Taken together, these results demonstrate that the dynamic characteristics are adjustable and designable of structural parameters in a specific frequency band and can provide a useful way to reduce the amplitude of resonant peaks and improve the vibration isolation performance for practical engineering applications.

Representing utility and deploying the body

2020 ◽  
Vol 43 ◽  
Author(s):  
David Spurrett
Keyword(s):  
Functional Activity ◽  
Degrees Of Freedom ◽  
The Body ◽  
Target Article ◽  
Processing Demands ◽  
The Many

Abstract Comprehensive accounts of resource-rational attempts to maximise utility shouldn't ignore the demands of constructing utility representations. This can be onerous when, as in humans, there are many rewarding modalities. Another thing best not ignored is the processing demands of making functional activity out of the many degrees of freedom of a body. The target article is almost silent on both.

Neuron-glia relationship during the early postembryonic development of the nervous system in some oligochaetes

1978 ◽  
Vol 36 (2) ◽  
pp. 600-601
Author(s):  
Wiktor Djaczenko ◽  
Carmen Calenda Cimmino
Keyword(s):  
Nervous System ◽  
Glial Cells ◽  
Early Period ◽  
Postembryonic Development ◽  
Ruthenium Red ◽  
The Body ◽  
Tubifex Tubifex ◽  
Growth Stages ◽  
Cell Processes ◽  
Cytoplasmic Processes

The simplicity of the developing nervous system of oligochaetes makes of it an excellent model for the study of the relationships between glia and neurons. In the present communication we describe the relationships between glia and neurons in the early periods of post-embryonic development in some species of oligochaetes.Tubifex tubifex (Mull. ) and Octolasium complanatum (Dugès) specimens starting from 0. 3 mm of body length were collected from laboratory cultures divided into three groups each group fixed separately by one of the following methods: (a) 4% glutaraldehyde and 1% acrolein fixation followed by osmium tetroxide, (b) TAPO technique, (c) ruthenium red method.Our observations concern the early period of the postembryonic development of the nervous system in oligochaetes. During this period neurons occupy fixed positions in the body the only observable change being the increase in volume of their perikaryons. Perikaryons of glial cells were located at some distance from neurons. Long cytoplasmic processes of glial cells tended to approach the neurons. The superimposed contours of glial cell processes designed from electron micrographs, taken at the same magnification, typical for five successive growth stages of the nervous system of Octolasium complanatum are shown in Fig. 1. Neuron is designed symbolically to facilitate the understanding of the kinetics of the growth process.

Flagellar Attachment in Selected Trypanosomes

1973 ◽  
Vol 31 ◽  
pp. 496-497
Author(s):  
J. J. Paulin
Keyword(s):  
Cell Membrane ◽  
Lateral Surface ◽  
The Body ◽  
Flagellar Pocket ◽  
Integral Component ◽  
Free Flagellum ◽  
Flagellar Axoneme

Movement in epimastigote and trypomastigote stages of trypanosomes is accomplished by planar sinusoidal beating of the anteriorly directed flagellum and associated undulating membrane. The flagellum emerges from a bottle-shaped depression, the flagellar pocket, opening on the lateral surface of the cell. The limiting cell membrane envelopes not only the body of the trypanosome but is continuous with and insheathes the flagellar axoneme forming the undulating membrane. In some species a paraxial rod parallels the axoneme from its point of emergence at the flagellar pocket and is an integral component of the undulating membrane. A portion of the flagellum may extend beyond the anterior apex of the cell as a free flagellum; the length is variable in different species of trypanosomes.

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