Encoding Words Into Interval Type-2 Fuzzy Sets Using an Interval Approach

This paper presents a very practical type-2-fuzzistics methodology for obtaining interval type-2 fuzzy set (IT2 FS) models for words, one that is called an interval approach (IA). The basic idea of the IA is to collect interval endpoint data for a word from a group of subjects, map each subject's data interval into a prespecified type-1 (T1) person membership function, interpret the latter as an embedded T1 FS of an IT2 FS, and obtain a mathematical model for the footprint of uncertainty (FOU) for the word from these T1 FSs. The IA consists of two parts: the data part and the FS part. In the data part, the interval endpoint data are preprocessed, after which data statistics are computed for the surviving data intervals. In the FS part, the data are used to decide whether the word should be modeled as an interior, left-shoulder, or right-shoulder FOU. Then, the parameters of the respective embedded T1 MFs are determined using the data statistics and uncertainty measures for the T1 FS models. The derived T1 MFs are aggregated using union leading to an FOU for a word, and finally, a mathematical model is obtained for the FOU. In order that all researchers can either duplicate our results or use them in their research, the raw data used for our codebook examples, as well as a MATLAB M-file for the IA, have been put on the Internet at: http://sipi.usc.edu/$sim$mendel.      

MEMS Sensor for In Situ TEM Atomic Force Microscopy

Here, we present a MEMS atomic force microscope sensor for use inside a transmission electron microscope (TEM). This enables direct in situ TEM force measurements in the nanonewton range and thus mechanical characterization of nanosized structures. The main design challenges of the system and sensor are to reach a high sensitivity and to make a compact design that allows the sensor to be fitted in the narrow dimensions of the pole gap inside the TEM. In order to miniaturize the sensing device, an integrated detection with piezoresistive elements arranged in a full Wheatstone bridge was used. Fabrication of the sensor was done using standard micromachining techniques, such as ion implantation, oxide growth and deep reactive ion etch. We also present in situ TEM force measurements on nanotubes, which demonstrate the ability to measure spring constants of nanoscale systems.     

Type-2 Fuzzistics for Nonsymmetric Interval Type-2 Fuzzy Sets: Forward Problems

Interval type-2 fuzzy sets (IT2 FS) play a central role in fuzzy sets as models for words and in engineering applications of T2 FSs. These fuzzy sets are characterized by their footprints of uncertainty (FOU), which in turn are characterized by their boundaries-upper and lower membership functions (MF). The centroid of an IT2 FS, which is an IT1 FS, provides a measure of the uncertainty in the IT2 FS. The main purpose of this paper is to quantify the centroid of a non-symmetric IT2 FS with respect to geometric properties of its FOU. This is very important because interval data collected from subjects about words suggests that the FOUs of most words are non-symmetrical. Using the results in this paper, it is possible to formulate and solve forward problems, i.e., to go from parametric non-symmetric IT2 FS models to data with associated uncertainty bounds. We provide some solutions to such problems for non-symmetrical triangular, trapezoidal, Gaussian and shoulder FOUs.     

Flexible Polymer Sensors for In Vivo Intravascular Shear Stress Analysis

Hemodynamic forces, specifically fluid shear stress, play an important role in the focal nature of arterial plaque formation known as atherosclerosis. We hereby developed biocompatible and flexible intravascular microelectromechanical systems sensor to measure real-time shear stress in the aortas of New Zealand white (NZW) rabbits. Titanium (Ti) and platinum (Pt) were deposited on silicon wafers and patterned to form the sensing elements. The polymer, parylene C, provided insulation to the electrode leads and flexibility to the sensors. Based on heat transfer principle, the heat dissipation from the sensors to the blood flow altered the resistance of the sensing elements, from which shear stress was calibrated. The resistance of the sensing element was measured at approximately 1.0 kOmega , and the temperature coefficient of resistance was at approximately 0.16%/^degC. The individual sensors were packaged to the catheter for intravascular deployment in the aortas of NZW rabbits (n = 5) . The sensor was capable of resolving spatial- and time-varying components of shear stress in the abdominal aorta. Computational fluid dynamic code based on non-Newtonian fluid properties showed comparable results within an acceptable range of experimental errors ( plusmn9%) for the maximal and minimal values in shear stress during one cardiac cycle. Therefore, we demonstrated the capability of biocompatible sensors for real-time shear stress measurement in vivo with a potential to advance the understanding between the blood flow and vascular disease.     

Image Stabilization for In Vivo Microscopy by High-Speed Visual Feedback Control

This paper presents image stabilization for microscopy using horizontal visual feedback control of the objective lens through a five-bar linkage and piezoelectric actuators, and its application to in vivo imaging. Even very small in vivo motion due to heartbeat and breathing makes microscopic observation difficult by blurring the microscope image or impossible by sending a region of interest out of view. In order to remove those unwanted effects of the motion, we have introduced motion-canceling robotic technologies into microscopy. Our image stabilization system through motion-canceling provides users with stabilized image sequences with respect to trembling of in vivo subjects. The developed image stabilization system, in term of robotics, corresponds to a visual feedback control system that consists of a robotic mechanism and a high-speed vision. A high-speed camera installed in the microscope detects the motion of the in vivo subject having topically applied fiducials. To virtually cancel this motion, we move the objective lens, synchronizing the motions of the subject and the lens to remove the relative motion between the two. As a result, we observe motion-free images to m. This technology is one of the very demanding technologies in biological research for in vivo observation with high resolution. In this paper, we verify the effectiveness of the developed system through in vivo experiments.     

Complexity Explained

This book provides a comprehensive and self-contained introduction to the field of pattern recognition (PR).     

Neisseria Meningitidis Detection Based on a Microcalorimetric Biosensor With a Split-Flow Microchannel

This paper proposes and demonstrates a novel microcalorimetric sensor for detecting Neisseria meningitidis. To eliminate additional heating structures and calibration steps, a split-flow microchannel is integrated into the microcalorimeter. The split-flow microchannel constantly maintains the output of the microcalorimeter near a zero level without the use of any heating elements when there is no biochemical reaction. With the use of the split-flow microchannel, an active heating element such as a heater is no longer required. In addition, to improve the sensitivity of the microcalorimeter, a thermal sensing component, which is a thermopile in this case, has been fabricated on a high thermal resistivity layer, which reduces the parasitic heat transfer to the silicon substrate and concentrates the released thermal energy to the thermopile. The characteristics of the proposed microcalorimeter were investigated by measuring the reaction heat of the biotin-streptavidin pairs. The sensitivity of the microcalorimeter was measured to be 0.21 V/cal. Then, a biological reaction between Neisseria meningitidis group B (NMGB) and its antibody was detected by using the proposed microcalorimeter. In order to verify the reliability of the measurement, exactly the same number of NMGB was reacted with its antibody and an optical density was measured by an enzyme-linked immunosorbent assay as a known reference.     

Adachi-Like Chaotic Neural Networks Requiring Linear-Time Computations by Enforcing a Tree-Shaped Topology

The Adachi neural network (AdNN) is a fascinating neural network (NN) which has been shown to possess chaotic properties, and to also demonstrate associative memory (AM) and pattern recognition (PR) characteristics. Variants of the AdNN have also been used to obtain other PR phenomena, and even blurring. An unsurmountable problem associated with the AdNN and the variants referred to above is that all of them require a quadratic number of computations. This is essentially because the NNs in each case are completely connected graphs. In this paper, we consider how the computations can be significantly reduced by merely using a linear number of computations. To achieves this, we extract from the original completely connected graph one of its spanning trees. We then address the problem of computing the weights for this spanning tree. This is done in such a manner that the modified tree-based NN has approximately the same input–output characteristics, and thus the new weights are themselves calculated using a gradient-based algorithm. By a detailed experimental analysis, we show that the new linear-time AdNN-like network possesses chaotic and PR properties for different settings. As far as we know, such a tree-based AdNN has not been reported, and the results given here are novel.      

Bottom-Up Construction of Minimum-Cost and/ or Trees for Sequential Fault Diagnosis

The problem of generating the sequence of tests required to reach a diagnostic conclusion with minimum average cost, which is also known as a test-sequencing problem, is considered. The traditional test-sequencing problem is generalized here to include asymmetrical tests. In general, the next test to execute depends on the results of previous tests. Hence, the test-sequencing problem can naturally be formulated as an optimal binary AND/OR decision tree construction problem, whose solution is known to be NP-hard. Our approach is based on integrating concepts from one-step look-ahead heuristic algorithms and basic ideas of Huffman coding to construct an AND/OR decision tree bottom-up as opposed to heuristics proposed in the literature that construct the AND/OR trees top-down. The performance of the algorithm is demonstrated on numerous test cases, with various properties.     

BLGAN: Bayesian Learning and Genetic Algorithm for Supporting Negotiation With Incomplete Information

Automated negotiation provides a means for resolving differences among interacting agents. For negotiation with complete information, this paper provides mathematical proofs to show that an agent's optimal strategy can be computed using its opponent's reserve price (RP) and deadline. The impetus of this work is using the synergy of Bayesian learning (BL) and genetic algorithm (GA) to determine an agent's optimal strategy in negotiation (N) with incomplete information. BLGAN adopts: (1) BL and a deadline-estimation process for estimating an opponent's RP and deadline and (2) GA for generating a proposal at each negotiation round. Learning the RP and deadline of an opponent enables the GA in BLGAN to reduce the size of its search space (SP) by adaptively focusing its search on a specific region in the space of all possible proposals. SP is dynamically defined as a region around an agent's proposal P at each negotiation round. P is generated using the agent's optimal strategy determined using its estimations of its opponent's RP and deadline. Hence, the GA in BLGAN is more likely to generate proposals that are closer to the proposal generated by the optimal strategy. Using GA to search around a proposal generated by its current strategy, an agent in BLGAN compensates for possible errors in estimating its opponent's RP and deadline. Empirical results show that agents adopting BLGAN reached agreements successfully, and achieved: (1) higher utilities and better combined negotiation outcomes (CNOs) than agents that only adopt GA to generate their proposals, (2) higher utilities than agents that adopt BL to learn only RP, and (3) higher utilities and better CNOs than agents that do not learn their opponents' RPs and deadlines.     

Linear QoS Goals of Additive and Concave Metrics in Ad Hoc Cognitive Packet Routing

This paper addresses two scalability problems related to the cognitive map of packets in ad hoc cognitive packet networks and proposes a solution. Previous works have included latency as part of the routing goal of smart packets, which requires packets to collect their arrival time at each node in a path. Such a requirement resulted in a packet overhead proportional to the path length. The second problem is that the multiplicative form of path availability, which was employed to measure resources, loses accuracy in long paths. To solve these problems, new goals are proposed in this paper. These goals are linear functions of low-overhead metrics and can provide similar performance results with lower cost. One direct result shown in simulation is that smart packets driven by a linear function of path length and buffer occupancy can effectively balance the traffic of multiple flows without the large overhead that would be needed if round-trip delay was used. In addition, energy-aware routing is also studied under this scheme as well as link selection based on their expected level of security     

The In-Situ Technique for Measuring Input Impedance and Connection Effects of RFID Tag Antenna

In this paper, an indirect noninvasive method for measuring input impedance and the variations in the assembly of the interconnect and packaging between antenna and the integrated circuit (IC) effects of passive radio frequency identification (RFID) transponder (tags) antennas is presented. The analysis of different RFID tags is presented together with the experimental data.     

Improved A Posteriori Speech Presence Probability Estimation Based on a Likelihood Ratio With Fixed Priors

In this paper, we present an improved estimator for the speech presence probability at each time-frequency point in the short-time Fourier transform domain. In contrast to existing approaches, this estimator does not rely on an adaptively estimated and thus signal-dependent a priori signal-to-noise ratio estimate. It therefore decouples the estimation of the speech presence probability from the estimation of the clean speech spectral coefficients in a speech enhancement task. Using both a fixed a priori signal-to-noise ratio and a fixed prior probability of speech presence, the proposed a posteriori speech presence probability estimator achieves probabilities close to zero for speech absence and probabilities close to one for speech presence. While state-of-the-art speech presence probability estimators use adaptive prior probabilities and signal-to-noise ratio estimates, we argue that these quantities should reflect true a priori information that shall not depend on the observed signal. We present a detection theoretic framework for determining the fixed a priori signal-to-noise ratio. The proposed estimator is conceptually simple and yields a better tradeoff between speech distortion and noise leakage than state-of-the-art estimators.     

A Multiobjective Evolutionary Conceptual Clustering Methodology for Gene Annotation Within Structural Databases: A Case of Study on the Gene Ontology Database

Current tools and techniques devoted to examine the content of large databases are often hampered by their inability to support searches based on criteria that are meaningful to their users. These shortcomings are particularly evident in data banks storing representations of structural data such as biological networks. Conceptual clustering techniques have demonstrated to be appropriate for uncovering relationships between features that characterize objects in structural data. However, typical conceptual clustering approaches normally recover the most obvious relations, but fail to discover the less frequent but more informative underlying data associations. The combination of evolutionary algorithms with multiobjective and multimodal optimization techniques constitutes a suitable tool for solving this problem. We propose a novel conceptual clustering methodology termed evolutionary multiobjective conceptual clustering (EMO-CC), relying on the NSGA-II multiobjective (MO) genetic algorithm. We apply this methodology to identify conceptual models in structural databases generated from gene ontologies. These models can explain and predict phenotypes in the immunoinflammatory response problem, similar to those provided by gene expression or other genetic markers. The analysis of these results reveals that our approach uncovers cohesive clusters, even those comprising a small number of observations explained by several features, which allows describing objects and their interactions from different perspectives and at different levels of detail.      

Brownian Microscopy for Simultaneous In Situ Measurements of the Viscosity and Velocity Fields in Steady Laminar Microchannel Flows

Brownian microscopy is an intriguing technique that enables in situ determination of the local fluid properties and velocity fields in microfluidic devices. We report application of Brownian microscopy to steady laminar microchannel flows and demonstrate accurate measurements of the viscosity of glycerin/water mixtures, as well as the velocity profiles. A theoretical model is developed to evaluate statistical errors in the measured Brownian diffusivity while accounting for complications associated with the electronic noise and finite exposure time of an imaging system. The model is validated using both Monte Carlo simulations and experiments.