Transformations for nonideal uniform circular arrays operating in correlated signal environments

The Davies transformation is a method to transform the steering vector of a uniform circular array (UCA) to one with Vandermonde form. As such, it allows techniques such as spatial smoothing for direction-of-arrival (DOA) estimation in a correlated signal environment, developed originally for uniform linear arrays, to be applied to UCAs. However, the Davies transformation can be highly sensitive to perturbations of the underlying array model. This paper presents a method for deriving a more robust transformation using optimization techniques. The effectiveness of the method is illustrated through a number of DOA estimation examples.     

Characterization of zero locations of polytopes of real polynomials

Given a stability region of a polytope of polynomials, the edge theorem can be used to verify certain root-clustering properties of the polytope. The authors consider the inverse problem; starting with a polytope of real polynomials, they present a result that allows the construction of the smallest set of regions in the complex plane which characterizes its zero location     

Real-Time Quality Evaluation of Wire Bonding Using Input Impedance

Input impedance characterizes the dynamic property of a linear system. A few existing technologies thus exploit input electrical impedance of wire bonders as the signature to monitor ultrasonic wire bonding processes. However, the waveforms of "impedance" in these technologies are evaluated only approximately. To overcome the shortcoming, we propose a method to detect the true waveforms of both the real and imaginary part of the input impedance. In the method, the voltage and current at the input port of a wire bonder are probed and processed to obtain impedance via Hilbert Transform. Because dynamics of a bonding process represented by these waveforms is fully responsible for the resulted bonding quality, a quality evaluation system based on pattern recognition of these waveforms is further proposed. An artificial neural network using back propagation as training scheme learns from a set of training data to correlate a few features of the impedance waveforms with the bonding strength of the corresponding bond identified by shearing tests. Through a set of verification data, the built system is validated to be capable of evaluating bonding quality right after a bonding process. The proposed method is not only in situ and real-time, but also sensorless, which means that the system is easy to be implemented without interfering operation     

Optimization and evaluation of reduced graphene oxide hydrogel composite as a demulsifier for heavy crude oil-in-water emulsion

The rising production of produced water from oilfields had been proven to bring detrimental environmental effects. In this study, an efficient, recyclable, and environmental-friendly reduced graphene oxide immobilized κ-Carrageenan hydrogel composite(κCa GO) was fabricated as an alternative sorbent for crude oil-in-water demulsification. Polyethyleneimine(PEI) was employed to form a stable hydrogel composite. The conditions for the immobilization of graphene oxide(GO) on PEI-modified κ-Carrageenan(κC) beads were optimized appropriately. An immobilization yield of 77% was attained at 2% PEI, 2 h immobilization activation time, and p H6.5. Moreover, the synthesized κCa GO is capable of demulsification with an average demulsification efficiency of 70%. It was found that the demulsification efficiency increases with salinity and κCa GO dosage, and it deteriorates under alkaline condition. These phenomena can be attributed to the interfacial interactions between κCa GO and the emulsion. Furthermore, the κCa GO can be recycled to use for up to six cycles without significant leaching and degradation. As such, the synthesized κCa GO could be further developed as a potential sorbent substitute for the separation of crude oil from produced water.     

A talking profile to distinguish identical twins

Identical twins pose a great challenge to face recognition due to high similarities in their appearances. Motivated by the psychological findings that facial motion contains identity signatures and the observation that twins may look alike but behave differently, we develop a talking profile to use the identity signatures in the facial motion to distinguish between identical twins. The talking profile for a subject is defined as a collection of multiple types of usual face motions from the video. Given two talking profiles, we compute the similarities of the same type of face motion in both profiles and then perform the classification based on those similarities. To compute the similarity of each type of face motion, we give higher weights to more abnormal motions which are assumed to carry more identity signature information.     

Analysis of two authorization protocols using Colored Petri Nets

To prevent unauthorized access to protected trusted platform module (TPM) objects, authorization protocols, such as the object-specific authorization protocol (OSAP), have been introduced by the trusted computing group (TCG). By using OSAP, processes trying to gain access to the protected TPM objects need to prove their knowledge of relevant authorization data before access to the objects can be granted. Chen and Ryan’s 2009 analysis has demonstrated OSAP’s authentication vulnerability in sessions with shared authorization data. They also proposed the Session Key Authorization Protocol (SKAP) with fewer stages as an alternative to OSAP. Chen and Ryan’s analysis of SKAP using ProVerif proves the authentication property. The purpose of this paper was to examine the usefulness of Colored Petri Nets (CPN) and CPN Tools for security analysis. Using OSAP and SKAP as case studies, we construct intruder and authentication property models in CPN. CPN Tools is used to verify the authentication property using a Dolev–Yao-based model. Verification of the authentication property in both models using the state space tool produces results consistent with those of Chen and Ryan.     

Robust Kalman filtering for uncertain discrete-time systems with probabilistic parameters bounded within a polytope

We consider the problem of finite horizon discrete-time Kalman filtering for systems with parametric uncertainties. Specifically, we consider unknown but deterministic uncertainties where the uncertain parameters are assumed to lie in a convex polyhedron with uniform probability density. The condition and a procedure for the construction of a suboptimal filter that minimizes an expected error covariance over-bound are derived.     

SNORE: spike noise removal and detection

A method for detection and removal of random spike noise in magnetic resonance (MR) raw data (k-space data) is described. This method would reduce or eliminate the corduroy-type and higher than usual level artifacts in MR images resulting from random spike noise in k-space data. The method described involves applying a spatially varying threshold to be k-space data. Any data point that has a magnitude greater than that of the threshold value at that location will be replaced by a local complex average of the neighboring data points or some other suitable data replacement scheme.     

Energy sector planning using multiple-index pinch analysis

Pinch analysis was initially developed as a methodology for optimizing energy efficiency in process plants. Applications of pinch analysis applications are based on common principles of using stream quantity and quality to determine optimal system targets. This initial targeting step identifies the pinch point, which then allows complex problems to be decomposed for the subsequent design of an optimal network using insights drawn from the targeting stage. One important class of pinch analysis problems is energy planning with footprint constraints, which began with the development of carbon emissions pinch analysis; in such problems, energy sources and demands are characterized by carbon footprint as the quality index. This methodology has been extended by using alternative quality indexes that measure different sustainability dimensions, such as water footprint, land footprint, emergy transformity, inoperability risk, energy return on investment and human fatalities. Pinch analysis variants still have the limitation of being able to use one quality index at a time, while previous attempts to develop pinch analysis methods using multiple indices have only been partially successful for special cases. In this work, a multiple-index pinch analysis method is developed by using an aggregate quality index, based on a weighted linear function of different quality indexes normally used in energy planning. The weights used to compute the aggregate index are determined via the analytic hierarchy process. A case study for Indian power sector is solved to illustrate how this approach allows multiple sustainability dimensions to be accounted for in energy planning.