DEVS-Based modeling of VoIP spam callers’ behavior for SPIT level calculation

At the present time the VoIP (Voice over Internet Protocol) service is generally accepted as an alternative for people seeking cheaper means to make a phone call. Users of VoIP service may fall anywhere along a spectrum between types at two extremes: one of which is an ordinary caller who doesn’t use the telephone for commercial purposes, while the other is a person who generates spam calls for commercial purposes. The focus of this paper concerns modeling of spam callers’ behavior to calculate the SPIT (Spam over Internet Telephony) level for management of the quality of service. From the perspective of a VoIP service provider’s view, spam callers are also a type of customer and sometimes they are valuable for increasing revenue. However, if a service provider does not manage spam calls, it can harm their business, because ordinary users might not receive phone calls using the phone numbers of the VoIP service. Thus, there is a trade-off between revenues and usability in managing spam calls in the VoIP service. This work presents a model of spam caller’s behavior using the DEVS (Discrete Event System Specification) formalism. The DEVS formalism is applicable as a model of behavior, by defining the state and state transition of the target of the model. In our model, we use six main parameters to define the states and state transitions. Each state is represented by a number which indicates the SPIT level of a caller. If the value is 1.0 then the caller is more similar to a spam caller. Based on the model definition, we constructed a SPIT level Calculation UI (User Interface) that is used to manage spam calls to improve VoIP service quality.     

Adaptive fuzzy IMM algorithm for uncertain target tracking

In real system application, the interacting multiple model (IMM)-based uncertain target tracking system operates with the following problems: it requires less computing resources as well as a robust performance with respect to the maneuvering such as a sub-model mismatched case, and further, it requires an easy design procedure related to its structures and parameters. To solve these problems, an adaptive fuzzy IMM (AFIMM) algorithm, which is based on well-defined basis sub-models and well-adjusted mode transition probabilities (MTPs), is proposed. The basis sub-models are defined by the detailed analysis in terms of kinematic models as well as the maneuvering property and the MTPs are adjusted by the fuzzy adaptor as well as the fuzzy decision maker. To verify the performance of the proposed algorithm, an airborne target tracking is performed. Simulation results show that the AFIMM effectively solves the problems experienced in the uncertain target tracking system online. Keywords: Adaptive fuzzy IMM, basis sub-models, mode transition probabilities, uncertain target.     

Feedback linearization vs. adaptive sliding mode control for a quadrotor helicopter

This paper presents two types of nonlinear controllers for an autonomous quadrotor helicopter. One type, a feedback linearization controller involves high-order derivative terms and turns out to be quite sensitive to sensor noise as well as modeling uncertainty. The second type involves a new approach to an adaptive sliding mode controller using input augmentation in order to account for the underactuated property of the helicopter, sensor noise, and uncertainty without using control inputs of large magnitude. The sliding mode controller performs very well under noisy conditions, and adaptation can effectively estimate uncertainty such as ground effects. Recommended by Editorial Board member Hyo-Choong Bang under the direction of Editor Hyun Seok Yang. This work was supported by the Korea Research Foundation Grant (MOEHRD) KRF-2005-204-D00002, the Korea Science and Engineering Foundation(KOSEF) grant funded by the Korea government(MOST) R0A-2007-000-10017-0 and Engineering Research Institute at Seoul National University. Daewon Lee received the B.S. degree in Mechanical and Aerospace Engineering from Seoul National University (SNU), Seoul, Korea, in 2005, where he is currently working toward a Ph.D. degree in Mechanical and Aerospace Engineering. He has been a member of the UAV research team at SNU since 2005. His research interests include applications of nonlinear control and vision-based control of UAV. H. Jin Kim received the B.S. degree from Korea Advanced Institute of Technology (KAIST) in 1995, and the M.S. and Ph.D. degrees in Mechanical Engineering from University of California, Berkeley in 1999 and 2001, respectively. From 2002–2004, she was a Postdoctoral Researcher and Lecturer in Electrical Engineering and Computer Science (EECS), University of California, Berkeley (UC Berkeley). From 2004–2009, she was an Assistant Professor in the School of in Mechanical and Aerospace Engineering at Seoul National University (SNU), Seoul, Korea, where she is currently an Associate Professor. Her research interests include applications of nonlinear control theory and artificial intelligence for robotics, motion planning algorithms. Shankar Sastry received the B.Tech. degree from the Indian Institute of Technology, Bombay, in 1977, and the M.S. degree in EECS, the M.A. degree in mathematics, and the Ph.D. degree in EECS from UC Berkeley, in 1979, 1980, and 1981, respectively. He is currently Dean of the College of Engineering at UC Berkeley. He was formerly the Director of the Center for Information Technology Research in the Interest of Society (CITRIS). He served as Chair of the EECS Department from January, 2001 through June 2004. In 2000, he served as Director of the Information Technology Office at DARPA. From 1996 to 1999, he was the Director of the Electronics Research Laboratory at Berkeley (an organized research unit on the Berkeley campus conducting research in computer sciences and all aspects of electrical engineering). He is the NEC Distinguished Professor of Electrical Engineering and Computer Sciences and holds faculty appointments in the Departments of Bioengineering, EECS and Mechanical Engineering. Prior to joining the EECS faculty in 1983 he was a Professor with the Massachusetts Institute of Technology (MIT), Cambridge. He is a member of the National Academy of Engineering and Fellow of the IEEE.     

Applications, techniques, and microfluidic interfacing for nanoscale biosensing

Biosensors based on nanotechnology are rapidly developing and are becoming widespread in the biomedical field and analytical chemistry. For these nanobiosensors to reach their potential, they must be integrated with appropriate packaging techniques, which are usually based on nano/microfluidics. In this review we provide a summary of the latest developments in nanobiosensors with a focus on label-based (fluorescence and nanoparticle) and label-free methods (surface plasmon resonance, micro/nanocantilever, nanowires, and nanopores). An overview on how these sensors interface with nano/microfluidics is then presented and the latest papers in the area summarized.     

Approximate probabilistic optimization using exact-capacity-approximate-response-distribution (ECARD)

Probabilistic structural design deals with uncertainties in response (e.g. stresses) and capacity (e.g. failure stresses). The calculation of the structural response is typically expensive (e.g., finite element simulations), while the capacity is usually available from tests. Furthermore, the random variables that influence response and capacity are often disjoint. In previous work we have shown that this disjoint property can be used to reduce the cost of obtaining the probability of failure via Monte Carlo simulations. In this paper we propose to use this property for an approximate probabilistic optimization based on exact capacity and approximate response distributions (ECARD). In Approximate Probabilistic Optimization Using ECARD, the change in response distribution is approximated as the structure is re-designed while the capacity distribution is kept exact, thus significantly reducing the number of expensive response simulations. ECARD may be viewed as an extension of SORA (Sequential Optimization and Reliability Assessment), which proceeds with deterministic optimization iterations. In contrast, ECARD has probabilistic optimization iterations, but in each iteration, the response distribution is approximated so as not to require additional response calculations. The use of inexpensive probabilistic optimization allows easy incorporation of system reliability constraints and optimal allocation of risk between failure modes. The method is demonstrated using a beam problem and a ten-bar truss problem. The former allocates risk between two different failure modes, while the latter allocates risk between members. It is shown that ECARD provides most of the improvement from risk re-allocation that can be obtained from full probabilistic optimization.     

Robust DOA estimation and target docking for mobile robots

Direction of arrival (DOA) guided automated target acquisition and docking system is proposed for mobile robots employing the dual-directional antenna system. The dual-directional antenna estimates the DOA of the signal of interest using the ratio of the signal strengths between two adjacent antennas. In practice, DOA estimation poses a significant technical challenge, since the RF signal is easily distorted by the environmental conditions. Therefore, the robot often loses its way in an electromagnetically disturbed environment. To cope with this problem, a robust DOA estimation algorithm is developed based on Kalman filtering. This algorithm allows the robot to reduce the potential error in the estimated DOA, and adjust the robot’s heading to the target transponder without needing to know the positions of current and previous measurements in a global coordinate system. The simulation and experiment results clearly demonstrate that the mobile robot equipped with the developed system is able to dock to a target transponder in an indoor environment partially occupied by obstacles.     

Proteomic analysis of sera of asymptomatic,early‐stage patients with Wilson's disease

Wilson's disease (WD) is characterized by excessive accumulation of intracellular copper in liver and extrahepatic tissues, leading to significant oxidative stress and tissue damage. To date, several diagnostic biomarkers for WD such as serum ceruloplasmin, serum or urine copper levels and copper content in liver have been identified. However, these biomarkers may not be convincing for the diagnosis in some WD patients. To identify additional novel diagnostic biomarkers, we compared the serum protein profiles of asymptomatic childhood WD patients (n=20), without neurologic manifestation or liver cirrhosis, with normal controls (n=13). Fourteen spots, five up‐regulated and nine down‐regulated (>2‐fold), were differentially expressed in WD patients in comparison to normal control on 2‐DE. Among them, three spots were down‐regulated in both male and female WD. MS/MS analysis revealed that the three spots were complement component C3, complement factor B and alpha‐2 macroglobulin. By comparative proteome analysis, complement component C3, complement factor B and alpha‐2 macroglobulin, which are related to oxidative stress and inflammation, turned out to be good candidates for novel diagnostic biomarkers for early stages of WD.     

Gait planning for quadruped robot based on dynamic stability: landing accordance ratio

In this article, the method for increasing dynamic stability of quadruped robot is proposed. Previous researches on dynamic walking of quadruped robots have used only walking pattern called central pattern generator (CPG). In this research, different from walking generation with only CPG, a instinctive stability measure called landing accordance ratio, is proposed and used for increasing dynamic stability. In addition, dynamic balance control and control to adjust walking trajectory for increasing dynamic stability measure is also proposed. Proposed methods are verified with dynamic simulation and a large number of experiments with quadruped robot platform.     

Toward the Proteome of the Human Peripheral Blood Eosinophil

Eosinophils (EOSs) are granular leukocytes that have significant roles in many inflammatory and immunoregulatory responses, especially asthma and allergic diseases. We have undertaken a fairly comprehensive proteomic analysis of purified peripheral blood EOSs from normal human donors primarily employing 2‐DE with protein spot identification by MALDI‐MS. Protein subfractionation methods employed included IEF (Zoom^® Fractionator) and subcellular fractionation using differential protein solubilization. We have identified 3141 proteins, which had Mascot expectation scores of 10^?3 or less. Of these 426 were unique and non‐redundant of which 231 were novel proteins not previously reported to occur in EOSs. Ingenuity Pathway Analysis showed that some 70% of the non‐redundant proteins could be subdivided into categories that are clearly related to currently known EOS biological activities. Cytoskeletal and associated proteins predominated among the proteins identified. Extensive protein posttranslational modifications were evident, many of which have not been previously reported that reflected the dynamic character of the EOS. This data set of eosinophilic proteins will prove valuable in comparative studies of disease versus normal states and for studies of gender differences and polymorphic variation among individuals.