Micro-prober for wafer-level low-noise measurements in MOS devices

Low-frequency noise measurements represent an interesting investigation technique for the characterization of the quality and reliability of microelectronic materials and devices. Performing meaningful noise measurements at low and very low (f<1 Hz) frequencies, however, may be quite challenging, particularly because of the many sources of interference that superimpose on the noise signal. For this reason, packaged samples are preferred because they allow accurate shielding from the external environment, and because keeping the sample in close proximity to the low-noise biasing system and amplifier reduces microphonic and electromagnetic disturbances. Notwithstanding this, the possibility of performing low-frequency noise measurements at wafer level would be quite interesting, both because of the ease of obtaining wafer-level samples from industries with respect to packaged samples, and because this would avoid possible packaging-process induced device degradation. The purpose of this work is to demonstrate that it is, in fact, possible to design and build a dedicated probe system for performing high-sensitivity, low-frequency noise measurements on metal-oxide-semiconductor devices at wafer level.     

Safe and effective navigation of autonomous robots in hazardous environments

The development of autonomous mobile machines to perform useful tasks in real work environments is currently being impeded by concerns over effectiveness, commercial viability and, above all, safety. This paper introduces a case study of a robotic excavator to explore a series of issues around system development, navigation in unstructured environments, autonomous decision making and changing the behaviour of autonomous machines to suit the prevailing demands of users. The adoption of the Real-Time Control Systems (RCS) architecture (Albus, 1991) is proposed as a universal framework for the development of intelligent systems. In addition it is explained how the use of Partially Observable Markov Decision Processes (POMDP) (Kaelbling et al., 1998) can form the basis of decision making in the face of uncertainty and how the technique can be effectively incorporated into the RCS architecture. Particular emphasis is placed on ensuring that the resulting behaviour is both task effective and adequately safe, and it is recognised that these two objectives may be in opposition and that the desired relative balance between them may change. The concept of an autonomous system having “values” is introduced through the use of utility theory. Limited simulation results of experiments are reported which demonstrate that these techniques can create intelligent systems capable of modifying their behaviour to exhibit either ‘safety conscious’ or ‘task achieving’ personalities.     

Biosensors and the clinical laboratory

Aside from the economic factors that make biosensors attractive, on-board signal conditioning and signal processing improve the limits of detection and simplify use of the devices. The present discussion summarizes the breadth of biosensor design and application, and the requirements of clinical assay detection. Current sensor research is aimed toward extending the lower limits of detection for nonradioactive immunoassays. The clinical laboratory is in a state of change; operating and instrumentation costs will affect the delivery of diagnostics. Technology will assume a major role in reshaping the clinical laboratory. Biosensors promise to deliver the diagnostic tools for the evolution that is now in progress. The clinical laboratory will no doubt continue to perform chemical profiling and the more specialized tests. The successful implementation of solid-state sensor technology promises to simplify immunoassay procedures, as the autoanalyzer did some 20 years ago for the profiling of blood metabolites. It is likely that more tests will be performed in physicians' offices with the advent of highly automated and cost-effective biosensors. By the use of this technology, practitioners of critical care medicine will be able to assume greater responsibility for diagnostic testing.     

Dimensioning and effective handling of signalling channels in a multimedia GEO Satellite platform

The deployment of highly powerful and sophisticated new-generation satellite broadband systems implies that a large portion of their bandwidth on the radio interface has to be devoted to conveying signalling information. Adequately dimensioning such a signalling bandwidth is an important design objective allowing the effective exploitation of the overall system resources and the cost-effective provision of a target quality of service to multimedia traffic. The performance of two basic techniques for accessing the signalling channels in a reference multimedia geostationary platform is investigated in this paper: random (slotted Aloha) and dedicated access. It is demonstrated here that dedicated access, despite rational appearances, allows 50% saving of signalling bandwidth while satisfying both the system and user constraints in terms of grade and quality of service respectively. This is accomplished by providing a statistical centralized connection admission control on Earth and an intelligent scheduling of the resource requests on board the satellite.     

Reverse logistics network design with stochastic lead times

This work is concerned with the efficient design of a reverse logistics network using an extended version of models currently found in the literature. Those traditional, basic models are formulated as mixed integer linear programs (MILP-model) and determine which facilities to open that minimize the investment, processing, transportation, disposal and penalty costs while supply, demand and capacity constraints are satisfied. However, we show that they can be improved when they are combined with a queueing model because it enables to account for (1) some dynamic aspects like lead time and inventory positions, and (2) the higher degree of uncertainty inherent to reverse logistics. Since this extension introduces nonlinear relationships, the problem is defined as a mixed integer nonlinear program (MINLP-model). Due to this additional complexity, the MINLP-model is presented for a single product-single-level network. Several examples are solved with a genetic algorithm based on the technique of differential evolution.     

Clinical Significance of Tumor Infiltrating Lymphocytes in Association with Hormone Receptor Expression Patterns in Epithelial Ovarian Cancer

Hormone receptor expression patterns often correlate with infiltration of specific lymphocytes in tumors. Specifically, the presence of specific tumor-infiltrating lymphocytes (TILs) with particular hormone receptor expression is reportedly associated with breast cancer, however, this has not been revealed in epithelial ovarian cancer (EOC). Therefore, we investigated the association between hormone receptor expression and TILs in EOC. Here we found that ERα, AR, and GR expression increased in EOC, while PR was significantly reduced and ERβ expression showed a reduced trend compared to normal epithelium. Cluster analysis indicated poor disease-free survival (DFS) in AR+/GR+/PR+ subgroup (triple dominant group); while the Cox proportional-hazards model highlighted the triple dominant group as an independent prognostic factor for DFS. In addition, significant upregulation of FoxP3+ TILs, PD-1, and PD-L1 was observed in the triple dominant group compared to other groups. NanoString analyses further suggested that tumor necrosis factor (TNF) and/or NF-κB signaling pathways were activated with significant upregulation of RELA, MAP3K5, TNFAIP3, BCL2L1, RIPK1, TRAF2, PARP1, and AKT1 in the triple dominant EOC group. The triple dominant subgroup correlates with poor prognosis in EOC. Moreover, the TNF and/or NF-κB signaling pathways may be responsible for hormone-mediated inhibition of the immune microenvironment.     

Characterizing the deformation of cold-rolled copper sheet

An advanced x-ray diffraction technique allows the development of texture components to be monitored and provides supplementary information about the cold work and residual stresses in cold-rolled copper sheet. The new technique confirms the results of an ultrasonic test, except for the ultrasonic test’s sensitivity to grain size. The new x-ray diffraction technique could be adapted to provide in-process, nondestructive evaluation of thin copper sheet, even for material moving at high speeds.     

Simulation of the S2 state multiline electron paramagnetic resonance signal of photosystem II: a multifrequency approach

The S2 state electron paramagnetic resonance (EPR) multiline signal of Photosystem II has been simulated at Q-band (35 Ghz), X-band (9 GHz) and S-band (4 GHz) frequencies. The model used for the simulation assumes that the signal arises from an essentially magnetically isolated MnIII-MnIV dimer, with a ground state electronic spin ST = 1/2. The spectra are generated from exact numerical solution of a general spin Hamiltonian containing anisotropic hyperfine and quadrupolar interactions at both Mn nuclei. The features that distinguish the multiline from the EPR spectra of model manganese dimer complexes (additional width of the spectrum (195 mT), additional peaks (22), internal "superhyperfine" structure) are plausibly explained assuming an unusual ligand geometry at both Mn nuclei, giving rise to normally forbidden transitions from quadrupole interactions as well as hyperfine anisotropy. The fitted parameters indicate that the hyperfine and quadrupole interactions arise from Mn ions in low symmetry environments, corresponding approximately to the removal of one ligand from an octahedral geometry in both cases. For a quadrupole interaction of the magnitude indicated here to be present, the MnIII ion must be 5-coordinate and the MnIV 5-coordinate or possibly have a sixth, weakly bound ligand. The hyperfine parameters indicate a quasi-axial anisotropy at MnIII, which while consistent with Jahn-Teller distortion as expected for a d4 ion, corresponds here to the unpaired spin being in the ligand deficient, z direction of the molecular reference axis. The fitted parameters for MnIV are very unusual, showing a high degree of anisotropy not expected in a d3 ion. This degree of anisotropy could be qualitatively accounted for by a histidine ligand providing pi backbonding into the metal dxy orbital, together with a weakly bound or absent ligand in the x direction.     

An Intelligent Man-Machine Dialogue System Based on AI Planning

We describe the modular architecture of a generic dialogue system that assists a user/operator in performing a task with a tool. This coaching system is named CALLIOPE after the Greek goddess of eloquence. It aims at being an active partner in an intelligent man-machine dialogue. The intelligent dimension of the coaching system is reflected by its ability to adapt to the user and the situation at hand. The CALLIOPE system contains an explicit user model and world model to situate its dialogue actions. A plan library allows it to follow loosely predetermined dialogue scenarios.The heart of the coaching system is an AI planning module, which plans a series of dialogue actions. We present a coherent set of three dialogue or speech actions that will make up the physical form of the man-machine communication.The use of the AI planning paradigm as a basis for man-machine interaction is motivated by research in various disciplines, as e.g., AI, Cognitive Science and Social Sciences. Starting from the man-man communication metaphor, we can view the thinking before speaking of a human communication partner as constructing an underlying plan which is responsible for the purposiveness, the organisation and the relevance of the communication.CALLIOPE has been fully implemented and tested on theoretical examples. At present, also three tailored versions of CALLIOPE are in operational use in different industrial application domains: operator support for remedying tasks in chemical process industry, operator support for a combined task of planning, plan execution and process control in the area of chemical process development, and thirdly decision support in production scheduling.     

Robust M-estimation of multivariate GARCH models

The Gaussian quasi-maximum likelihood estimator of Multivariate GARCH models is shown to be very sensitive to outliers in the data. A class of robust M-estimators for MGARCH models is developed. To increase the robustness of the estimators, the use of volatility models with the property of bounded innovation propagation is recommended. The Monte Carlo study and an empirical application to stock returns document the good robustness properties of the M-estimator with a fat-tailed Student t loss function.