The proper interaction of bone tissue - the natural porous biomaterial - with a porous coated intra-osseous implant is conditioned, among others, by the implant porous coating poroaccessibility for bone tissue adaptive ingrowth. The poroaccessibility is the ability of implant porous coating outer layer to accommodate the ingrowing bone tissue filling in its pore space and effective new formed bone mineralizing in the pores to form a biomechanically functional bone-implant fixation. The functional features of the microtopography of intra-osseous implant porous surfaces together with the porosity of pore space of the outer layer of the porous coating are called by bioengineers the porostructural-osteoconductive properties of the porous coated implant. The properties are crucial for successful adaptive bone tissue ingrowth and further long-term (secondary) biomechanical stability of the boneimplant interface. The poroaccessibility of intra-osseous implants porous coating outer layers is characterized by - the introduced in our previous papers - set of stereometric parameters of poroaccessibility: the effective volumetric porosity fVef, the index of the porous coating space capacity VPM, the representative surface porosity fSrep, the representative pore size pSrep, the representative angle of the poroaccessibility Wrep and the bone-implant interface adhesive surface enlargement index y. Presented in this paper, an original method of evaluation of the porostructural-osteoconductive properties of intra-osseous implant porous coatings outer layer by means of the parameters of poroaccessibility was preliminary verified during experimental tests performed on the representative examples of porous coated femoral stems and acetabular cups of various hip endoprostheses. The computer-aided stereometric evaluation of the microstructure of implant porous coatings outer layer can be now realized by the authoring application software PoroAccess_1.0 elaborated in our research team in Java programming language

The three-dimensional (3D) coordinate measurement of radio frequency identification (RFID) multi-tag

networks is one of the important issues in the field of RFID, which affects the reading performance of

RFID multi-tag networks. In this paper, a novel method for 3D coordinate measurement of RFID multitag

networks is proposed. A dual-CCD system (vertical and horizontal cameras) is used to obtain images of

RFID multi-tag networks from different angles. The iterative threshold segmentation and the morphological

filtering method are used to process the images. The template matching method is respectively used to

determine the two-dimensional (2D) coordinate and the vertical coordinate of each tag. After that, the

3D coordinate of each tag is obtained. Finally, a back-propagation (BP) neural network is used to model

the nonlinear relationship between the RFID multi-tag network and the corresponding reading distance.

The BP neural network can predict the reading distances of unknown tag groups and find out the optimal

distribution structure of the tag groups corresponding to the maximum reading distance. In the future work,

the corresponding in-depth research on the neural network to adjust the distribution of tags will be done.