With the expanding usage of various devices, which emit static and extremely low-frequency magnetic fields, increases the number of biomedical reports on their influence as well as demand for suitable experimental exposure systems. Experimental setups range from permanent magnets through Helmholtz coils to solenoids; however, almost all of them provide relatively weak magnetic fields of up to 10 mT. Widespread use of devices such as MR scanners imposes intentional as well as unintentional exposure to stronger fields. A typical solenoid produces stronger although less homogeneous field than the most commonly used experimental equipment composed of sets of coaxial coils. In order to provide scalable, relatively strong, low-varying field within experimental volume that is large enough for in vivo as well as in vitro experiments modified solenoids are considered. Variation of the field was reduced by modifying the shape and size of solenoid's cross section. Modified solenoids were modeled analytically, numerically, and as a prototype. Solenoid geometries were optimized for maximal field performance and minimal power consumption. The optimal modified solenoids scalable to desired sizes of the experimental volume and values of maximal magnetic induction intensities are offered. The suggested solenoid modification method can decrease field variation as much as 10.6 times.

Among the many categories of images that require lossless compression, medical images can be indicated as one of the most important category. Medical image compression with loss impairs of diagnostic value, therefore, there are often legal restrictions on the image compression with losses. Among the common approaches to medical image compression we can distinguish the transformation-based and prediction-based approaches. This paper presents algorithms for the prediction based on the edge detection and estimation of local gradient. Also, a novel prediction algorithm based on advantages of standardized median predictor and gradient predictor is presented and analyzed. Removed redundancy estimation was done by comparing entropies of the medical image after prediction.