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> Phenomena > Phenomenon or Process > Acceleration

Acceleration

Acceleration is the process of increasing speed, velocity, or rate of change over time.
It is a fundamental concept in physics and engineering, describing the rate at which an object's velocity changes.
Acceleration can be caused by external forces acting on an object, such as gravity or friction, or by changes in the object's own internal energy.
Understanding and quantifying acceleration is crucial in fields like transportation, aerodynamics, and robotics, where efficiency and safety depend on precise control of motion.
Researchers and engineers utilize various techniques and technologies to measure, analyze, and optimize acceleration in a wide range of applications, from high-performance vehicles to cutting-edge industrial machinery.
By studying and manipulating acceleration, scientists and innovators can drive advancements that enhance speed, power, and productivity acros many domains.

Most cited protocols related to «Acceleration»

1
RELION-3: Advanced Algorithmic Innovations
Besides the developments described above, RELION-3 comprises two major algorithmic advances that have already been described elsewhere.
Firstly, Bayesian particle polishing implements a Gaussian Process regression algorithm for estimating beam-induced motion tracks for individual particles, and an improved B-factor estimation algorithm for resolution-dependent weighting of individual movie frames. Although not strictly necessary, Bayesian polishing is typically performed after motion-correction at the micrograph (patch) level by MotionCor2 (Zheng et al., 2017 (link)). To expose the metadata of this algorithm better to the Bayesian polishing approach, we also implemented our own version of the MotionCor2 algorithm. Unlike the UCSF implementation, our version does not use GPU-acceleration but runs on multi-core CPUs using OpenMP multi-threading. For 4⁢k×4⁢k movies, and using 12 cores, our program runs at comparable speeds to the UCSF implementation. More details about the Bayesian polishing and our implementation of the MotionCor2 algorithm have been described by Zivanov et al, (2018) (link).
Secondly, multi-body refinement implements an automated and iterative approach for focused refinement with partial signal subtraction on multiple independently moving parts of a complex (Nakane et al., 2018 (link)). This approach is useful for improving densities of flexible multi-domain complexes, and yields insights into the type of inter-domain motions that are present in those.
In addition, many smaller programs and functionalities have been added throughout the single-particle processing workflow. We highlight the following. The motion correction, CTF estimation and auto-picking jobs now output PDF files with metadata plots for all micrographs. We have improved our 3D initial model generation by adopting a stochastic gradient descent (SGD) procedure that follows the implementation in cryoSPARC much more closely than our previous version. We achieved considerably speedups in 2D and 3D classification by using subsets of particles in the first few iterations, which was inspired by a similar implementation in cistem (Grant et al., 2018 (link)). We have added a program called relion_star_handler that implements a range of useful operations for metadata files in the star format (Hall, 1991 (link)). And we have implemented a program called relion_align_symmetry that aligns the symmetry axes of a 3D map in arbitrary orientation according to the conventions in RELION.
Zivanov J., Nakane T., Forsberg B.O., Kimanius D., Hagen W.J., Lindahl E, & Scheres S.H. (2018). New tools for automated high-resolution cryo-EM structure determination in RELION-3. eLife, 7, e42166.
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Publication 2018
Acceleration Complement Factor B Conferences Epistropheus Human Body physiology Reading Frames
2
Optimized Diffusion Imaging for Tractography
These are the protocols recommended for tractography on the FMRIB-center 3 T Siemens Verio scanner. All scanning was performed using monopolar Stejskal–Tanner (ST) diffusion weighting, a single shell with a b-value of 1500 s/mm2, 60 distinct diffusion gradients optimized on a half sphere and then re-distributed on the whole sphere and four b = 0 images interspersed. The total of 64 scans were repeated twice with opposing phase-encode directions (A→P and P→A) and using GRAPPA with an in-plane acceleration of 2. No multi-band (across plane acceleration) was used.
Andersson J.L, & Sotiropoulos S.N. (2016). An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging. Neuroimage, 125, 1063-1078.
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Publication 2016
Acceleration Diffusion Radionuclide Imaging
3
Simultaneous Measurement of Physical Activity and Energy Expenditure
Study participants spent approximately 24-h period in a whole-room indirect calorimeter (28 (link)), and followed a structured protocol for simultaneous measurements of PA and EE. The protocol included a broad range of pursuits ranging from moderate and vigorous to light and sedentary tasks, including eating meals and snacks and self-care activities. During times (30 to 120 minutes) when no activity was specifically scheduled, the participants were asked to engage in their normal daily routine as much as possible without specific suggestions. They also recorded their activities in a diary with a detailed schedule, reporting any episodes of accidental monitor nonwear intervals and other relevant comments. Sleep was defined as the period of time spent lying on a mattress at night between 9:00 pm and 6:00 am without any significant movement as determined by the floor (force platform) in the room calorimeter. The participants were instructed how to record their activities in a provided diary with a detailed schedule and a timeline. They checked off each scheduled activity and reported any episodes of accidental monitor nonwear intervals and other relevant information (e.g. treadmill speed) or comments. During the day, staff was available for assistance and the dairy was discussed with each participant after finishing the study.
Body weight was measured to the nearest 0.01 kg with a digital scale and height was measured using a wall-mounted stadiometer. The minute-to-minute EE was calculated from the rates of oxygen consumption and carbon dioxide production (33 (link)). Nonwear EE was calculated by summing EE measured by the room calorimeter during time intervals detected as nonwear by each algorithm.
The PA was measured by commercially available Actigraph GT1M accelerometer (ActiGraph, Pensacola, FL), calibrated by the manufacturer placed on the anterior axillary line of the hip on the dominant side of the body. Among commercially available accelerometers, the Actigraph used in the present study provides consistent and high quality data, supported by its feasibility, reliability and validity (9 (link)). The monitor reports counts from the summation of the measured accelerations over a specified epoch (1 ). Actigraph data were collected at a 1-second epoch and summed as counts per minute.
Choi L., Liu Z., Matthews C.E, & Buchowski M.S. (2011). Validation of Accelerometer Wear and Nonwear Time Classification Algorithm. Medicine and science in sports and exercise, 43(2), 357-364.
Publication 2011
Acceleration Accidents Actigraphy Axilla Body Weight Carbon dioxide EPOCH protocol Human Body Light Movement Oxygen Consumption Sleep Snacks TimeLine
4
RELION-3: Accelerated Cryo-EM Analysis
The large computational load of the cryo-EM pipeline requires RELION to be highly parallel. In RELION-2, a new code-path was introduced, which off-loaded the core computations onto graphical processing units (GPUs) (Kimanius et al., 2016 (link)). This new path was by design less demanding of double precision arithmetics and memory, and provided a large improvement in data throughput by virtue of the greatly increased speed. However, the GPU acceleration is only available for cards from a single vendor, and it cannot use many of the large-scale computational resources available in existing centres, local clusters, or even researchers’ laptops. In addition, the memory available on typical GPUs limits the box sizes that can be used, which could turn into a severe bottleneck for large particles. For RELION-3, we have developed a new general code path where CPU algorithms have been rewritten to mirror the GPU acceleration, which provides dual execution branches of the code that work very efficiently both on accelerators as well as the single-instruction, multiple-data vector units present on traditional CPUs. This has enabled lower precision arithmetics, it reduces memory requirements considerably, and it also makes it possible to exploit the very large amounts of memory that can be fitted on CPU servers. In addition, it provides a transparent source code where new algorithms may be accelerated immediately, and it enables a significant speedup compared to the legacy code path, which is nonetheless still present in RELION-3 for comparative purposes.
Zivanov J., Nakane T., Forsberg B.O., Kimanius D., Hagen W.J., Lindahl E, & Scheres S.H. (2018). New tools for automated high-resolution cryo-EM structure determination in RELION-3. eLife, 7, e42166.
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Publication 2018
Acceleration Cloning Vectors Memory
5
Negative Staining Procedure for EM
Unless stated otherwise, specimens were prepared for EM using the conventional negative staining procedure. Briefly, a 2.5 ml drop of sample solution was adsorbed to a glow-discharged carbon-coated copper grid, washed with two drops of deionized water, and stained with two drops of freshly prepared 0.75% uranyl formate.
Unless stated otherwise, samples were imaged at room temperature using a Philips Tecnai T12 electron microscope equipped with an LaB6 filament and operated at an acceleration voltage of 120 kV. Images were taken at a magnification of 52,000x and a defocus value of 1.5 mm on Kodak SO-163 film using low-dose procedures. Films were developed for 12 minutes with fullstrength Kodak D-19 developer at 20°C. All micrographs were visually inspected with a laser diffractometer, and only drift-free images were selected for digitization with a Zeiss SCAI scanner using a step size of 7 mm. Micrographs were binned over 3 ¥ 3 pixels to yield a pixel size of 4.04 Å on the specimen level.
Ohi M., Li Y., Cheng Y, & Walz T. (2004). Negative Staining and Image Classification – Powerful Tools in Modern Electron Microscopy. Biological Procedures Online, 6, 23-34.
Publication 2004
Acceleration Carbon Copper Cytoskeletal Filaments Electron Microscopy uranyl formate

Most recents protocols related to «Acceleration»

1
Infrasound Detection using Fiber Flow Sensors
Not available on PMC !

Example 2

In some applications, an infrasonic sensor is desired, with a frequency response fl that extends to an arbitrarily low frequency, such as a tenth of hundredth of a Hertz. Such a sensor might be useful for detecting fluid flows associated with movement of objects, acoustic impulses, and the like. Such an application works according to the same principles as the sonic sensor applications, though the length of individual runs of fibers might have to be greater.

In addition, the voltage response of the electrode output to movements is proportional to the velocity of the fiber, and therefore one would typically expect that the velocity of movement of fluid particles at infrasonic frequencies would low, leading to low output voltages. However, in some applications, the fluid movement is macroscopic, and therefore velocities may be appreciable. For example, in wake detection applications, the amplitude may be quite robust.

Generally, low frequency sound is detected by sensors which are sensitive to pressure such as infrasound microphones and microbarometers. As pressure is a scaler, multiple sensors should be used to identify the source location. Meanwhile, due to the long wave length of low frequency sound, multiple sensors have to be aligned far away to distinguish the pressure difference so as to identify the source location. As velocity is a vector, sensing sound flow can be beneficial to source localization. There is no available flow sensor that can detect infrasound flow in a broad frequency range with a flat frequency response currently. However, as discussed above, thin fibers can follow the medium (air, water) movement with high velocity transfer ratio (approximate to 1 when the fiber diameter is in the range of nanoscale), from zero Hertz to tens of thousands Hertz. If a fiber is thin enough, it can follow the medium (air, water) movement nearly exactly. This provides an approach to detect low frequency sound flow directly and effectively, with flat frequency response in a broad frequency range. This provides an approach to detect low frequency sound flow directly. The fiber motion due to the medium flow can be transduced by various principles such as electrodynamic sensing of the movement of a conductive fiber within a magnetic field, capacitive sensing, optical sensing and so on. Application example based on electromagnetic transduction is given. It can detect sound flow with flat frequency response in a broad frequency range.

For the infrasound detection, this can be used to detect manmade and natural events such as nuclear explosion, volcanic explosion, severe storm, chemical explosion. For the source localization and identification, the fiber flow sensor can be applied to form a ranging system and noise control to find and identify the low frequency source. For the low frequency flow sensing, this can also be used to detect air flow distribution in buildings and transportations such as airplanes, land vehicles, and seafaring vessels.

The infrasound pressure sensors are sensitive to various environmental parameters such as pressure, temperature, moisture. Limited by the diaphragm of the pressure sensor, there is resonance. The fiber flow sensor avoids the key mentioned disadvantages above. The advantages include, for example: Sensing sound flow has inherent benefit to applications which require direction information, such as source localization. The fiber flow sensor is much cheaper to manufacture than the sound pressure sensor. Mechanically, the fiber can follow the medium movement exactly in a broad frequency range, from infrasound to ultrasound. If the fiber movement is transduced to the electric signal proportionally, for example using electromagnetic transduction, the flow sensor will have a flat frequency response in a broad frequency range. As the flow sensor is not sensitive to the pressure, it has a large dynamic range. As the fiber motion is not sensitive to temperature, the sensor is robust to temperature variation. The fiber flow sensor is not sensitive to moisture. The size of the flow sensor is small (though parallel arrays of fibers may consume volume). The fiber flow sensor can respond to the infrasound instantly.

Note that a flow sensor is, or would be, sensitive to wind. The sensor may also respond to inertial perturbances. For example, the pressure in the space will be responsive to acceleration of the frame. This will cause bulk fluid flows of a compressible fluid (e.g., a gas), resulting in signal output due to motion of the sensor, even without external waves. This can be advantages and disadvantages depends on the detailed applications. For example, it can be used to detect flow distribution in the buildings. If used to detect infrasound, the wind influence be overcome by using an effective wind noise reduction approach.

US11869476B2. Acoustic metamaterial (2024-01-09). The Research Foundation for The State University of New York [US]. Inventors: Steven M. Hoffberg [US].
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Patent 2024
Acceleration Acoustics A Fibers Blast Injuries Blood Vessel Cloning Vectors Dietary Fiber Electric Conductivity Electricity Electromagnetics Fibrosis Magnetic Fields Movement Pressure Reading Frames Sound Sound Waves Toxic Epidermal Necrolysis Ultrasonics Vaginal Diaphragm Vibration Water Movements Wind
2
MEMS-Enabled Vehicle Attitude Control
Not available on PMC !

Example 13

A vehicle according to this application example includes any of the MEMS devices described above and an attitude control section that performs attitude control based on a detection signal output from the MEMS device.

The vehicle is mounted with the MEMS device in which the rotational displacement of the movable body in the in-plane direction of the major surface is restricted and which can continuously detect acceleration or the like even when an excessive impact is applied, and the attitude control section performing attitude control based on the detection signal output from the MEMS device. Therefore, it is possible to increase the reliability of the vehicle mounted with the MEMS device described above.

US11867716B2. MEMS device, electronic apparatus, and vehicle (2024-01-09). SEIKO EPSON CORPORATION [JP]. Inventors: Fumiya Ito [JP].
Full text: Click here
Patent 2024
Acceleration Human Body Medical Devices Signal Detection (Psychology)
3
MEMS Device with Restricted Rotational Displacement
Not available on PMC !

Example 12

An electronic apparatus according to this application example includes any of the MEMS devices described above and a control section that performs control based on a detection signal output from the MEMS device.

The electronic apparatus includes the MEMS device in which the rotational displacement of the movable body in the in-plane direction of the major surface is restricted and which can continuously detect acceleration or the like even when an excessive impact is applied, and the control section performing control based on the detection signal output from the MEMS device. Therefore, it is possible to increase the reliability of the electronic apparatus mounted with the MEMS device described above.

US11867716B2. MEMS device, electronic apparatus, and vehicle (2024-01-09). SEIKO EPSON CORPORATION [JP]. Inventors: Fumiya Ito [JP].
Full text: Click here
Patent 2024
Acceleration Human Body Medical Devices Signal Detection (Psychology)
4
Adaptive Activation Mechanism via Motion Characteristics
Not available on PMC !

Example 7

In this example a more practical approach is shown of the mechanism of Example 7 above where activation results when one motion characteristic, e.g., acceleration, influences the threshold of activation for sensing on another motion characteristic, e.g., velocity.

As shown in FIG. 14, the activation mechanism may comprise a pawl 50 mounted on a rotating disk 51 that is retained by a bias element 52 (magnet, spring etc.) between the pawl 50 and an inertial mass 53 also mounted on the rotating disk 51. When the disk 51 is spinning, a centripetal force is present on the pawl 50 that acts against the bias element 52. Another bias element 54 is present between the rotating disk 51 and the inertial mass 53. Upon rotational acceleration of the disk 51, the inertial mass 53 acts against the bias element 52 and rotates relative to the disk 51, moving the pawl 50 bias element 52 closer to the pawl 50 pivot axis 55, thereby reducing the restraining torque and lowering the rotational velocity at which the pawl 50 overcomes the bias element 52. When the disk 51 is spinning at a constant velocity or subjected to only a small acceleration the bias element 52 between the pawl 50 and the inertial mass 53 remains further away from the pawl 50 pivot axis 55, thus providing a higher restraining torque, requiring a greater rotational velocity for the pawl 50 to overcome the bias element 52. When the pawl 50 overcomes the bias element 52 a secondary stopping or braking system 56 is engaged (activation).

The possible resulting profiles for the above mechanism, assuming velocity and acceleration are the motion characteristics might look as per:

FIG. 15 where the threshold T is reached when a combination of high acceleration and low velocity occurs, the profile changing via a curved path;

FIG. 16 where the threshold T is reached when a combination of high acceleration and low velocity occurs, the profile changing in a linear step manner; or

FIG. 17 where the threshold T is reached when a combination of a high acceleration and high velocity occurs and the profile changing in a relatively linear manner. As should be appreciated, the exact profile will be dependent on the system dynamics.

US11878651B2. Variable behavior control mechanism for a motive system (2024-01-23). EDDY CURRENT LIMITED PARTNERSHIP [NZ]. Inventors: Andrew Karl Diehl [NZ], Peter Scott [NZ], Benjamin Woods [NZ], Lincoln Frost [NZ].
Full text: Click here
Patent 2024
Acceleration Behavior Control Epistropheus Motivation SERPINA3 protein, human Torque
5
Maintaining Capacitance Stability in MEMS Devices
Not available on PMC !

Example 11

In the MEMS device according to the application example, it is preferable that the abutment portion and the movable body are at the same potential.

According to this application example, the movable body and the abutment portion are at the same potential, so that the fluctuation and loss of a capacitance generated between the movable body and the abutment portion can be suppressed when the both portions come into contact with each other. Hence, when the movable body and the abutment portion come into contact with each other, the measurement of a physical quantity such as acceleration can be continuously performed.

US11867716B2. MEMS device, electronic apparatus, and vehicle (2024-01-09). SEIKO EPSON CORPORATION [JP]. Inventors: Fumiya Ito [JP].
Full text: Click here
Patent 2024
Acceleration Human Body Medical Devices Physical Examination

Top products related to «Acceleration»

1
S 4800 by Hitachi
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The S-4800 is a high-resolution scanning electron microscope (SEM) manufactured by Hitachi. It provides a range of imaging and analytical capabilities for various applications. The S-4800 utilizes a field emission electron gun to generate high-quality, high-resolution images of samples.
2
32 channel head coil by Siemens
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The 32-channel head coil is a key component in magnetic resonance imaging (MRI) systems. It is designed to acquire high-quality images of the human head, enabling detailed visualization and analysis of brain structure and function.
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Jem 2100f by JEOL
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The JEM-2100F is a transmission electron microscope (TEM) designed and manufactured by JEOL. It is capable of high-resolution imaging and analytical capabilities. The JEM-2100F is used for a variety of research and industrial applications that require advanced electron microscopy techniques.
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The JEM-2100 is a transmission electron microscope (TEM) manufactured by JEOL. It is designed to provide high-quality imaging and analysis of a wide range of materials at the nanoscale level. The instrument is equipped with a LaB6 electron source and can operate at accelerating voltages up to 200 kV, allowing for the investigation of a variety of samples.
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The Zetasizer Nano ZS is a dynamic light scattering (DLS) instrument designed to measure the size and zeta potential of particles and molecules in a sample. The instrument uses laser light to measure the Brownian motion of the particles, which is then used to calculate their size and zeta potential.
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More about "Acceleration"

Acceleration is the rate of change in velocity or speed over time, a fundamental concept in physics and engineering.
It is caused by external forces like gravity or friction, or by changes in an object's internal energy.
Understanding and quantifying acceleration is crucial in fields like transportation, aerodynamics, and robotics, where efficiency and safety depend on precise motion control.
Researchers and engineers utilize various techniques and technologies to measure, analyze, and optimize acceleration, from high-performance vehicles to cutting-edge industrial machinery like the S-4800 scanning electron microscope and MAGNETOM Prisma MRI scanner.
By studying and manipulating acceleration, scientists and innovators can drive advancements that enhance speed, power, and productivity across many domains.
Key acceleration-related topics include kinematics, dynamics, inertia, momentum, and Newton's laws of motion.
Specialized equipment like the 32-channel head coil, MATLAB software, and D8 Advance X-ray diffractometer help researchers quantify and analyze acceleration.
Electron microscopes like the JEM-2100F and JEM-1400Plus provide high-resolution imaging to study accelerative forces at the nano-scale.
Cutting-edge tools like the Zetasizer Nano ZS and SU8010 scanning electron microscope enable researchers to measure, model, and optimize acceleration in diverse applications, from material science to robotics.
By leveraging the power of AI-driven platforms like PubCompare.ai, scientists can accelerate their research, enhance reproducibility, and drive innovation in the field of acceleration and motion control.