Magnet 2.8.0 __LINK__

As of now, I have the .ivy2 folder, but there are no folders inside that folder - only a file called .sbt.ivy.lock. I tried this URL from the error above and it let me download a file: -sbt.org/scalasbt/sbt-plugin-releases/com.typesafe.play/sbt-plugin/scala_2.12/sbt_1.0/2.8.0/ivys/ivy.xml

Magnet 2.8.0

To introduce the state-space control design method, we will use the magnetically suspended ball as an example. The current through the coils induces a magnetic force which can balance the force of gravity and cause the ball (which is made of a magnetic material) to be suspended in mid-air. The modeling of this system has been established in many control text books (including Automatic Control Systems by B. C. Kuo, the seventh edition).

where is the vertical position of the ball, is the current through the electromagnet, is the applied voltage, is the mass of the ball, is the acceleration due to gravity, is the inductance, is the resistance, and is a coefficient that determines the magnetic force exerted on the ball. For simplicity, we will choose values = 0.05 kg, = 0.0001, = 0.01 H, = 1 Ohm, = 9.81 m/s^2. The system is at equilibrium (the ball is suspended in mid-air) whenever = (at which point = 0). We linearize the equations about the point = 0.01 m (where the nominal current is about 7 Amps) and obtain the linear state-space equations:

It looks like the distance between the ball and the electromagnet will go to infinity, but probably the ball hits the table or the floor first (and also probably goes out of the range where our linearization is valid).

Let's build a controller for this system using a pole placement approach. The schematic of a full-state feedback system is shown below. By full-state, we mean that all state variables are known to the controller at all times. For this system, we would need a sensor measuring the ball's position, another measuring the ball's velocity, and a third measuring the current in the electromagnet.

When we can't measure all state variables (often the case in practice), we can build an observer to estimate them, while measuring only the output . For the magnetic ball example, we will add three new, estimated state variables () to the system. The schematic is as follows:

In previous versions of MRTK (HoloToolkit and MRTK v2), all packages were released as a complete set, marked with the same version number (ex: 2.8.0). Starting with MRTK3, each package will be individually versioned, following the Semantic Versioning 2.0.0 specification.

Also called counter electromotive force, back-EMF, or CEMF. Back-EMF is a voltage (in V), a difference of potential. In the coils of a motor, it appears while electrons are moving through the wires while under a motion of magnetic field (Lenz's law) may explain it better than me).

Let's precise that only the permanent magnets produce a "static" magnetic field. The rest, particularly the magnetic field from the winding, is more likely to be called electromagnetic. We will see that in a short while.

Anyway, all this magnetic mess is not disappearing away. It forms imaginary lines from one pole to another (negative to positive), which we call a magnetic field, all around the motor. Its strength is measured in Amperes per meter (A/m). The representation of lines, though imaginary, is an excellent model to help to understand how a magnetic field would look if you had superpowers.

Any magnetic material (e.g., an iron-made object) put near a motor will interact with its magnetic field. Although no material can stop and cancel a magnetic field, it's possible to create shields that can redirect the magnetic lines.

These kinds of interference are not really welcomed, if not unwanted. They can highly affect electronic circuits to go as far as stopping working. Fortunately, electromagnetic shields can be applied to reduce their propagation.

Hepatic nodules can be detected with ultrasound, computed tomography (CT), or magnetic resonance (MR) imaging. Ultrasound lacks the ability to discriminate iron content, and the sensitivity of computed tomography for the detection of iron is also limited [7]. MR T2* weighted imaging, on the other hand, is considered to be the most sensitive technique for the visualization of iron-containing hepatic nodules, while T2* mapping methods can be used to quantify diffuse hepatic iron concentration. Susceptibility-weighted imaging (SWI) is more sensitive to detecting focal iron rich lesions by adding phase information in addition to T2* contrast. It has been well documented in the brain that SWI is superior to T2* and other existing MRI techniques for the detection of iron content, hemorrhage, and calcification [8], [9]. This is because phase data is an additional source of information about local susceptibility changes induced by iron, calcium or deoxyhemoglobin in various physiological or pathological conditions. For example, SWI can detect as many as 5 times more cerebral hemorrhagic lesions in patients with diffuse axonal injury than conventional T2* weighted imaging [10].

SWI postprocessing was performed automatically on the magnet. Postprocessing of abdominal SWI images was designed to reduce motion artifacts from breathing and cusp artifact (also termed singularity artifact) from B0 inhomogeneity, both of which are increased in abdominal imaging compared to brain imaging. First, complex images from each of the 12 channels were acquired individually and high-pass filtered with a 3232 filter to reduce artifacts related to both coil sensitivity and magnetic field inhomogeneity. High-pass filtering was achieved by the following steps: 1) Fourier transformation of the complex image to k-space; 2) while keeping the center of k-space (size 3232) unchanged, the remainder of the k-space matrix was zero-filled; 3) inverse Fourier transform of the zero-filled k-space to obtain a complex low-pass filter, and division of the original complex image with this low-pass filter to remove low spatial frequency components. Then, the high-pass filtered complex images from each channel were weighted by the coil sensitivity factor and combined to generate a single complex image, as described by the adaptive combine method [28]. The phase image from this final channel-combined complex image was extracted and used to create a positive phase mask such that the mask value would be for phase >0 and be 1 otherwise. Finally, the SWI image was created by multiplying this phase mask four times to the magnitude image [29]. The main difference between this process and brain SWI [29] is the filtering of the multichannel data prior to recombination. Additionally, SWI parameters such as resolution, flip angle (the main determinant of contrast in magnitude images), and echo time (main determinate of contrast for phase images) were evaluated to determine the optimal experimental parameters for hepatic imaging in a typical clinical setting.

Our results of this study evaluating 46 patients with hepatic cirrhosis and siderotic nodules demonstrated that abdominal SWI detects more nodules than other tested sequences; in particular, almost six times as many patients (35.5 vs. 6, using averaged reader statistics, Table 2) were categorized as having diffuse SN (>20 nodules per slice) than T2*-weighted images with long echo times (TE), the current standard. Not only was SWI able to detect more lesions, but SWI also demonstrated significantly better lesion conspicuity than other tested sequences. These findings suggest that SWI has a greater sensitivity for the detection of SN than T2*-weighted imaging, likely due to the improved sensitivity for the detection of iron content. At 3T, both SWI and T2*-weighted images have an echo time of 10 ms; which is equivalent to an echo time of 20 ms at 1.5T when considering susceptibility effects. T2*-weighted demonstrates 80% sensitivity for SN detection at 1.5 T, and longer echo times (e.g., 15 ms at 1.5T) may further increase sensitivity [17], [22]. Increased conspicuity on SWI images can be explained by the enhancement of susceptibility effects when using processed phase information in combination with magnitude images. Previous theoretical work has indicated that even an object measuring less than 25% of a voxel can have a conspicuous appearance if sufficiently paramagnetic [32].

We are aware of several limitations in this study. While T2* weighted imaging is the most common clinically used method to detect siderotic nodules, the gold standard for siderotic nodule detection remains histologic; pathological correlation with explant livers would be the ideal reference; lack of pathologic correlation potentially biases results to favor T2* and SWI over other imaging techniques. We also considered all hypointense lesions on T2* and SWI to be siderotic nodules; while this is most likely given our patient subset, other lesions can also appear hypointense. Only histologic explant liver evaluation could definitively exclude all possible nonsiderotic hypointense nodules. The inclusion of patients only with known siderotic nodules introduces a bias, and our evaluation cannot conclusively prove whether SWI would detect siderotic nodules in instances where other MRI imaging methods would detect none; T2* weighted MRI detected at least 5 or more siderotic nodules in all patients. Since only a single representative image from each examination was chosen for evaluation, the potential for undersampling bias exists; evaluation of the entire hepatic volume would have eliminated this possible bias. The use of multiple breath-hold imaging acquisitions to achieve coverage of the entire liver may be difficult or unattainable in some cirrhotic patients, such as those with ascites or reduced pulmonary function. Gastrointestinal air is a known source of magnetic susceptibility and can impart artifacts, and can limit evaluation of some liver regions depending upon anatomic considerations; however, we anticipated that further technological improvements in MR sequence design can minimize such artifacts. 041b061a72