Three-dimensional medical image recognition of cancer of the liver by a revised radial basis function (RBF) neural network algorithm

In this study, we propose a revised radial basis function (RBF) neural network algorithm and apply this algorithm to computer-aided diagnosis (CAD) of the liver. First, the revised RBF neural network algorithm is applied to recognition of the liver regions, and the recognition results are compared with those obtained using the conventional RBF neural network and the conventional multilayered neural network trained using the back-propagation algorithm. It is shown that the revised RBF neural network is accurate, and is a useful method because the parameters are automatically determined. Then, the revised RBF neural network is applied to CAD of the liver cancer called hepatocellular carcinoma (HCC).     

Derivation and implementation of a cone-beam reconstruction algorithm for nonplanar orbits

B.D. Smith (ibid., vol.MI-4, p.15-25, 1985; Opt. Eng., vol.29, p.524-34, 1990) and P. Grangeat (These de doctorat, 1987; Lecture Notes in Mathematics 1497, p.66-97, 1991) derived a cone-beam inversion formula that can be applied when a nonplanar orbit satisfying the completeness condition is used. Although Grangeat's inversion formula is mathematically different from Smith's one, they have similar overall structures to each other. The contribution of the present paper is two-fold. First, based on the derivation of Smith, the authors point out that Grangeat's inversion formula and Smith's one can be conveniently described using a single formula (the Smith-Grangeat inversion formula) that is in the form of space-variant filtering followed by cone-beam back projection. Furthermore, the resulting formula is reformulated for data acquisition systems with a planar detector to obtain a new reconstruction algorithm. Second, the authors make two significant modifications to the new algorithm to reduce artifacts and numerical errors encountered in direct implementation of the new algorithm. As for exactness of the new algorithm, the following fact can be stated. The algorithm based on Grangeat's intermediate function is exact for any complete orbit, whereas that based on Smith's intermediate function should be considered as an approximate inverse excepting the special case where almost every plane in 3D space meets the orbit. The validity of the new algorithm is demonstrated by simulation studies.     

Photo-regulated metal coordination of azobenzene polymer having sterically controlled ion binding sites

Summary 2,2-Dimethylazobenzenes having metal chelating ligands at meta-positions were synthesized, and the interaction with metal ions were estimated for syn- and anti-ligands on thermal isomerization. The azobenzenes with the metal-interaction were transformed to vinyl azobenzenes and copolymerized with styrene. The metal extracting ability was found in photo-isomerized polymer having syn-bis(iminodiacetic acid) groups; the selectivity was high for Cu(II) ion.     

Prototyping and modeling of the centrifugal casting process for paraffin waxes

Centrifugal casting is a technology used for manufacturing hybrid rocket paraffin grains. This technology helps avoiding voids formation inside the solid paraffin as it cools. Voids are formed because of air bubbles being entrapped while pouring and because the liquid wax shrinks by 17–19% upon cooling. In this work, the centrifugal casting process for the manufacturing of paraffin cylinders was prototyped at two different scales considering critical casting issues. The effects of process parameters (rotational speed, melt temperature, and flow rate) on the tensile properties of the manufactured grains were analyzed. The results of the optimization conducted at the lower scale (2.5?kg) were up scaled to manufacture 25?kg grains. The resulting mechanical properties complied with the design specifications, and they were better than those characterized from the gravity cast wax. A numerical model of growth and dissolution of bubbles during the process was then developed to predict the quality of the castings. The numerical results showed how increasing the mold rotational speed up to 1800?rpm reduced the removal time. However, compared to grains solidification time, the predicted removal times were much shorter, proving the advantage of centrifugal casting in counteracting voids formation.     

Virtual segment: Store–carry–forward relay-based support for wide-area non-real-time data exchange

We have proposed the concept of the virtual segment (VS), in which a global communication service is provided by combining a store–carry–forward scheme using vehicles with broadband wireless/wired network infrastructures along roads connected to the Internet. The VS can be a practical framework for non-real-time, asynchronous message transfer (especially for large messages) in a cost-effective manner. In this study, a critical implementation design issue, the message forward scheduling, in the VS approach is discussed and investigated through computer simulation by our developed VS simulator that has reflected the results of the field experiment for realistic performance evaluation.     

Codevelopmental learning between human and humanoid robot using a dynamic neural-network model.

This paper examines characteristics of interactive learning between human tutors and a robot having a dynamic neural-network model, which is inspired by human parietal cortex functions. A humanoid robot, with a recurrent neural network that has a hierarchical structure, learns to manipulate objects. Robots learn tasks in repeated self-trials with the assistance of human interaction, which provides physical guidance until the tasks are mastered and learning is consolidated within the neural networks. Experimental results and the analyses showed the following: 1) codevelopmental shaping of task behaviors stems from interactions between the robot and a tutor; 2) dynamic structures for articulating and sequencing of behavior primitives are self-organized in the hierarchically organized network; and 3) such structures can afford both generalization and context dependency in generating skilled behaviors.     

Selectivity and stability via dendritic nonlinearity

Inspired by recent studies regarding dendritic computation, we constructed a recurrent neural network model incorporating dendritic lateral inhibition. Our model consists of an input layer and a neuron layer that includes excitatory cells and an inhibitory cell; this inhibitory cell is activated by the pooled activities of all the excitatory cells, and it in turn inhibits each dendritic branch of the excitatory cells that receive excitations from the input layer. Dendritic nonlinear operation consisting of branch-specifically rectified inhibition and saturation is described by imposing nonlinear transfer functions before summation over the branches. In this model with sufficiently strong recurrent excitation, on transiently presenting a stimulus that has a high correlation with feed- forward connections of one of the excitatory cells, the corresponding cell becomes highly active, and the activity is sustained after the stimulus is turned off, whereas all the other excitatory cells continue to have low activities. But on transiently presenting a stimulus that does not have high correlations with feedforward connections of any of the excitatory cells, all the excitatory cells continue to have low activities. Interestingly, such stimulus-selective sustained response is preserved for a wide range of stimulus intensity. We derive an analytical formulation of the model in the limit where individual excitatory cells have an infinite number of dendritic branches and prove the existence of an equilibrium point corresponding to such a balanced low-level activity state as observed in the simulations, whose stability depends solely on the signal-to-noise ratio of the stimulus. We propose this model as a model of stimulus selectivity equipped with self-sustainability and intensity-invariance simultaneously, which was difficult in the conventional competitive neural networks with a similar degree of complexity in their network architecture. We discuss the biological relevance of the model in a general framework of computational neuroscience.     

Business process integration as a solution to the implementation of supply chain management systems

In the domain of supply chain management (SCM), various software packages have been developed for planning business strategies. To solve the problem of system productivity in applying planning packages, we propose a solution concept, business process integration (BPI), which fuses workflow and enterprise application integration (EAI) technology. Two characteristic policies are included in BPI. The first is to design the minimum set of business processes for real-time information sharing with planning packages without changing other processes. The second is to integrate several systems with EAI technology and to manage their execution with a workflow tool. Based on these policies, we propose various design templates and integration adapters. Our evaluation shows that using BPI, a target system can be developed with less manpower, in less time, and with higher quality than previous methods.