An architecture framework for an adaptive extensible processor

To improve the performance of embedded processors, an effective technique is collapsing critical computation subgraphs as application-specific instruction set extensions and executing them on custom functional units. The problem with this approach is the immense cost and the long times required to design a new processor for each application. As a solution to this issue, we propose an adaptive extensible processor in which custom instructions (CIs) are generated and added after chip-fabrication. To support this feature, custom functional units are replaced by a reconfigurable matrix of functional units (FUs). A systematic quantitative approach is used for determining the appropriate structure of the reconfigurable functional unit (RFU). We also introduce an integrated framework for generating mappable CIs on the RFU. Using this architecture, performance is improved by up to 1.33, with an average improvement of 1.16, compared to a 4-issue in-order RISC processor. By partitioning the configuration memory, detecting similar/subset CIs and merging small CIs, the size of the configuration memory is reduced by 40%.     

Transflective IPS‐LCD with improved reflective contrast ratio

Abstract— In order to improve the reflective contrast ratio of transflective IPS‐LCDs, a novel pixel design for a normally white reflective IPS has been proposed. In this design, the large‐inter‐electrode‐spacing layout using a novel driving method and a double‐layered electrode have effectively reduced the light leakage. By applying these two technologies, a transflective IPS‐LCD has been successfully demonstrated with a high contrast ratio (15:1) in the reflective mode and a wide‐viewing‐angle characteristic in the transmissive mode.     

Quantitative imaging of Young's modulus of solids: a contact-mechanics study

We developed equipment and methods for measuring quantitatively the local Young's modulus of solids. It consists of an electrodeless langasite oscillator and line antennas, and oscillator vibrations are generated and detected contactlessly. A constant biasing force results from oscillator mass and is independent of surface roughness. The effect of material anisotropy on the measured stiffness is theoretically discussed for studying the limitation of the quantitative measurement. The microscopy has been applied to polycrystalline copper, and the measured modulus is compared to calculations based on electron-backscatter-diffraction measurements. Also, we applied it to a duplex stainless steel and an embedded silicon-carbide fiber. The results reveal textured regions, defects with high sensitivity, and even stiffness distribution in a single grain.     

A low wall-loading DEMO reactor design with high priority for early and reliable realization of a tokamak fusion reactor over the cost performance

Based on scientific databases adopted for designing ITER plasmas and on the advancement of fusion nuclear technology from the recent R&D program, a low wall-loading DEMO fusion reactor has been designed, where high priority has been given to the early and reliable realization of a tokamak fusion plasma over the cost performance. Since the major radius of this DEMO reactor is chosen to be 10 m, plasma ignition is achievable with a low fusion power of 0.8 GW and an operation period of 4–5 hours is available only with inductive current drive. The low ignition power makes it possible to adopt a first wall with an austenitic stainless steel, for which significant databases and operating experience exists, due to its use in the presence of neutron irradiation in fission reactors. In step with development of advanced materials, a step-wise increase of the fusion power seems to be feasible and realistic, because this DEMO reactor has the potential to produce a fusion power of 5 GW.     

Self-identification algorithm for zeolite-based thermal capacity gas sensor

Microsystem Technologies - We demonstrate a new operation mode of thermal gas sensor based on thermal capacity extraction with identification algorithm. The system is a silicon microstructure...     

Mechanical property enhancement of aligned multi-walled carbon nanotube sheets and composites through press-drawing process

A solid-state drawing and winding process was done to create thin aligned carbon nanotube (CNT) sheets from CNT arrays. However, waviness and poor packing of CNTs in the sheets are two main weaknesses restricting their reinforcing efficiency in composites. This report proposes a simple press-drawing technique to reduce wavy CNTs and to enhance dense packing of CNTs in the sheets. Non-pressed and pressed CNT/epoxy composites were developed using prepreg processing with a vacuum-assisted system. Effects of pressing on the mechanical properties of the aligned CNT sheets and CNT/epoxy composites were examined. Pressing with distributed loads of 147, 221, and 294 N/m showed a substantial increase in the tensile strength and the elastic modulus of the aligned CNT sheets and their composites. The CNT sheets under a press load of 221 N/m exhibited the best mechanical properties found in this study. With a press load of 221 N/m, the pressed CNT sheet and its composite, respectively, enhanced the tensile strength by 139.1 and 141.9%, and the elastic modulus by 489 and 77.6% when compared with non-pressed ones. The pressed CNT/epoxy composites achieved high tensile strength (526.2 MPa) and elastic modulus (100.2 GPa). Results show that press-drawing is an important step to produce superior CNT sheets for development of high-performance CNT composites.     

Production of IL-6 by T cells from the femoral head of patients with rapidly destructive coxopathy (RDC)

We developed a method to improve the quantitative precision of FDG-PET scans in cancer patients. The total-lesion evaluation method generates a correlation coefficient (r) constrained Patlak parametric image of the lesion together with three calculated glucose metabolic indices: (a) the total-lesion metabolic index ("KT-tle", ml/min/lesion); (b) the total-lesion voxel index ("VT-tle", voxels/lesion); and (c) the global average metabolic index ("KV-tle", ml/min/voxel). METHODS: The glucose metabolic indices obtained from conventional region of interest (ROI) and multiplane evaluation were used as standards to evaluate the accuracy of the total-lesion evaluation method. Computer simulations and four patients with metastatic melanoma before and after chemotherapy were studied. RESULTS: Computer simulations showed that the total-lesion evaluation method has improved precision (% s.d. < 0.6%) and accuracy (approximately 10% error) compared with the conventional ROI method (% s.d. approximately 5%; approximately 25% error). The KT-tle and VT-tle indices from human FDG-PET studies using the total-lesion evaluation method showed excellent correlations with the corresponding values obtained from the conventional ROI methods and multiplane evaluation (r approximately 1.0) and CT lesion volume measurements. CONCLUSION: This method is a simple but reliable way to quantitatively monitor tumor FDG uptake. The method has several advantages over the conventional ROI method: (a) less sensitive to the ROI definition, (b) no need for image registration of serial scan data and (c) includes tumor volume changes in the global tumor metabolism.     

Measurement of aluminum or silicon activity in Fe-Ni (< 30 %) alloys using mullite and ZrO2-based solid electrolyte galvanic cell

Using mullite (3Al₂O₃·2SiO₂)-tube type and ZrO₂ (MgO mole fractions of 9 %)-plug type solid electrolyte, the activities of Al in the Fe-15, 29% Ni-0.002 to 0.84% Al (mass contents in%) alloys were measured at 1873 K in an Al₂O₃ crucible, and those of Si in the Fe-13, 28% Ni-0.13 to 2.3% Si alloys were measured at 1823 K in a mullite crucible with an excess of SiO₂. The interaction coefficients of and were determined as ?0.0173 ± 0.0052 and 0.00021 ± 0.00007, respectively, and those of and were determined as 0.0094 ± 0.0049 and ?0.00008 ± 0.00004, respectively. The oxygen activities obtained by the EMF measurements were compared with those obtained from the contents of analyzed oxygen. The oxygen activities in the Fe-Ni-Al alloys were found to be supersaturated. The interaction coefficients of and were determined as 0.0027 ± 0.0012 and 0.00012 ± 0.00004, respectively, from the contents of analyzed oxygen in Fe-Ni-Si alloys.     

Robust stability of nonlinear plants with a non-symmetric Prandtl-Ishlinskii hysteresis model

In this paper, robust stability of nonlinear plants represented by non-symmetric Prandtl-Ishlinskii (PI) hysteresis model is studied. In general, PI hysteresis model is the weighted superposition of play or stop hysteresis operators, and the slopes of the operators are considered to be the same. In order to make a hysteresis model, a modified form of non-symmetric play hysteresis operator with unknown slopes is given. The hysteresis model is described by a generalized Lipschitz operator term and a bounded parasitic term. Since the generalized Lipschitz operator is unknown, a new condition using robust right coprime factorization is proposed to guarantee robust stability of the controlled plant with the hysteresis nonlinearity. As a result, based on the proposed robust condition, a stabilized plant is obtained. A numerical example is presented to validate the effectiveness of the proposed method.     

Identification of a golf swing robot using soft computing approach

Golf swing robots have been recently developed in an attempt to simulate the ultra high-speed swing motions of golfers. Accurate identification of a golf swing robot is an important and challenging research topic, which has been regarded as a fundamental basis in the motion analysis and control of the robots. But there have been few studies conducted on the golf swing robot identification, and comparative analyses using different kinds of soft computing methodologies have not been found in the literature. This paper investigates the identification of a golf swing robot based on four kinds of soft computing methods, including feedforward neural networks (FFNN), dynamic recurrent neural networks (DRNN), fuzzy neural networks (FNN) and dynamic recurrent fuzzy neural networks (DRFNN). The performance comparison is evaluated based on three sets of swing trajectory data with different boundary conditions. The sensitivity of the results to the changes in system structure and learning rate is also investigated. The results suggest that both FNN and DRFNN can be used as a soft computing method to identify a golf robot more accurately than FFNN and DRNN, which can be used in the motion control of the robot.