In a strap down MAGNETIC compass, heading angle is estimated using the Earth's MAGNETIC field measured by Three-Axis Magnetometers (TAM). However, due to several inevitable errors in the MAGNETIC system, such as sensitivity errors, non-orthogonal and misalignment errors, hard iron and soft iron errors, measurement noises and local MAGNETIC fields, there are large error between the magnetometers' outputs and actual geoMAGNETIC field vector. This is the necessity of MAGNETIC CALIBRATION of TAM, especially in navigation application to achieve the true heading angle. In this paper, two methodologies, including clustering swinging method and clustering velocity vector method are presented for MAGNETIC compass CALIBRATION. Several factors for clustering process have been introduced and analyzed. The algorithms can be applied in both low-cost MEMS magnetometer and high-accuracy MAGNETIC sensors. The proposed CALIBRATION algorithms have been evaluated using in-ground and in-flight tests. It can be concluded from the experimental results that, applying the clustering CALIBRATION algorithms bring about a considerable enhancement in the accuracy of MAGNETIC heading angle.

Heading estimation is one of the main challenges in low-cost inertial navigation systems (INSs). Non-observability of heading angle with gravitational acceleration vector as well as inaccessibility of radio/satellite navigation in underwater vehicles increases the value of this challenge. Applying three-axis magnetometer and heading estimation from earth MAGNETIC field components is one of the main approaches to accuracy enhancement of the low-cost inertial navigation systems. However, in order to achieve accurate heading estimation, the magnetometer must be appropriately calibrated for both sensor errors and presence of MAGNETIC deviations. This paper aims to develop back-stepping algorithm for magnetometer CALIBRATION applied to measure the earth MAGNETIC field components. In the proposed algorithm, the results of the prevalent spherical MAGNETIC CALIBRATION are corrected based on vertical channel decomposition and MAGNETIC field components in the horizontal plane. The algorithm is evaluated in the field tests executed on an Autonomous Underwater Vehicle (AUV).

Background and Aims: Disc displacement is the most common temporomandibular joint disorder and MAGNETIC resonance imaging (MRI) is the gold standard in its diagnosis. This disorder can lead to changes in signal intensity of MAGNETIC resonance (MR). The purpose of this study was evaluation of correlation between relative signal intensity of MR images of retrodiscal tissue, superior and inferior head of lateral ptrygoid muscle with type of anterior disk displacement and condylar head flattening in patients with temporomandibular disorder (TMD).Materials and Methods: In this retrospective study, 31 MR images of patients who had anterior disc displacement were evaluated. After relative signal intensity measurement for retrodiscal tissue, superior and inferior head of lateral ptrygoid muscle, the correlation between relative signal intensity and type of anterior disc displacement was evaluated with repeated measure ANOVA test. In each of these 3 areas, t-test was used to compare the groups with and without condylar head flattening.Results: The correlation between relative signal intensity of MR images and type of anterior disc displacement in retrodiscal tissue, superior and inferior head of lateral ptrygoid muscle was not significant. There was also no statistically significant correlation between relative signal intensity of MR images and flattening of condylar head in retrodiscal tissue, superior and inferior head of lateral ptrygoid muscle (P>0.05).Conclusion: According to findings of this study, relative signal intensity of MR images in retrodiscal tissue, superior and inferior head of ptrygoid muscle is not a good predictor for type of anterior disc displacement and flattening of condylar head. It seems that this cannot be used as a diagnostic marker for TMD progression.

Using 48016 synthetic maximum Wood-Anderson amplitudes read from waveforms of 2650 events recorded by stations of Iranian Seismological Center (IRSC, irsc. ut. ac. ir), Iranian National Seismograph Network (INSN, www. iiees. ac. ir) and temporary seismic networks belong to Institute for Advanced Studies in Basic Sciences (IASBS, iasbs. ac. ir), the empirical attenuation curve ( 0  log A ) for local magnitude of Iran has been calculated as follows: 0 log (1. 556 0. 06) log (0. 001637 0. 0009) ( 100) 3 100 R A R              where R is hypocentral distance in km and 0 A is maximum displacement amplitude of shear wave in millimeter. The empirical attenuation relationship is valid for hypocentral distances equal or smaller than 800 km. ML amplitude is the maximum amplitude observed on a Wood-Anderson (W-A) seismogram. We manually picked the maximum amplitudes on the shear window of synthetic W-A seismograms having S/N of larger than 5. We calculated synthetic W-A seismograms by removing the instrument response of each record and convolving the resulting signal with the response of the standard W-A torsion seismograph. We assumed a static magnification of 2080 for the W-A instrument. The selected ML amplitudes are belonging to events at hypocentral distance of 10 to 800 km. Except for the Makran and South Caspian Basin regions, the ray coverage of the ML amplitude covers properly the whole Iranian Plateau. To reduce the scatter of magnitude residues and ensure a reliable calculation of the attenuation curve, the selected events belong to 45 precisely relocated seismic clusters with location uncertainties of 5 km or less. The cluster approach produces redundancy in amplitudes arriving from a cluster to a given station. The redundancy will facilitate easy recognition and removal of possible outliers and thus provide a reliable estimate for the magnitude station correction. The magnitude station corrections attempts to absorb the regional attenuation difference relative to that dictated by average attenuation relationship derived in this work. The calculated attenuation curve shows a larger geometrical spreading for hypocentral distances closer than 100 km, representing a super-spherical geometrical spreading, and a smaller value for intrinsic attenuation for distances farther than 200 km once compared with the currently used ML relationship of Hutton and Boore (1987). Excluding amplitudes with hypocentral distances smaller than 60 km results in a geometrical spreading coefficient close to spherical spreading, emphasizing the importance of near distances data on accurate estimation of the geometrical spreading value. The difference in the attenuation parameters between our results and those of Hutton and Boore (1987) relationship clearly indicates the crustal disparity of Iranian Plateau and southern California. This necessitates using the new attenuation relationship for Iran. We calculated the local magnitude empirical attenuation relationship by inverting the amplitude data set for the geometrical spreadin]g and intrinsic attenuation. We did not consider magnitude station corrections in our inversion to avoid any tradeoff between the station corrections and attenuation parameters. We have shown that the magnitude residuals calculated by our local magnitude empirical relationship do not vary systemically versus hypocentral distance or magnitude. Due to the cluster-wise approach in selection of our events and partially because of the precise location of the selected events, the standard deviation of magnitude residues is about 0. 19, significantly smaller than those reported by others. We calculated the station corrections by averaging the magnitude residual in each station. The station corrections vary between-0. 44 to 0. 32. Generally, stations located in Zagros, Alborz and north west of Iran have negative station correction representing amplitude amplification in these regions relative to central Iran and north east of Iran. The new attenuation relationship provides better estimates for the attenuation parameters and especially provides precise magnitudes at close hypocentral distances. By time, the expansion of Iranian seismic networks reduces the average distance spacing of Iranian seismic stations and thus usage of better local magnitude formula such as ours becomes more important.