A revised particle swarm optimization based discrete Lagrange multipliers method for nonlinear programming problems

In this paper, a new algorithm for solving constrained nonlinear programming problems is presented. The basis of our proposed algorithm is none other than the necessary and sufficient conditions that one deals within a discrete constrained local optimum in the context of the discrete Lagrange multipliers theory. We adopt a revised particle swarm optimization algorithm and extend it toward solving nonlinear programming problems with continuous decision variables. To measure the merits of our algorithm, we provide numerical experiments for several renowned benchmark problems and compare the outcome against the best results reported in the literature. The empirical assessments demonstrate that our algorithm is efficient and robust.     

C-AMTE: A location mechanism for flexible cache management in chip multiprocessors

This paper describes Constrained Associative-Mapping-of-Tracking-Entries (C-AMTE), a scalable mechanism to facilitate flexible and efficient distributed cache management in large-scale chip multiprocessors (CMPs). C-AMTE enables fast locating of cache blocks in CMP cache schemes that employ one-to-one or one-to-many associative mappings. C-AMTE stores in per-core data structures tracking entries to avoid on-chip interconnect traffic outburst or long distance directory lookups. Simulation results using a full system simulator demonstrate that C-AMTE achieves improvement in cache access latency by up to 34.4%, close to that of a perfect location strategy.     

He’s homotopy perturbation method: A strongly promising method for solving non-linear systems of the mixed Volterra–Fredholm integral equations

In this paper, He’s homotopy perturbation method is applied to solve non-linear systems of mixed Volterra–Fredholm integral equations. Two examples are presented to illustrate the ability of the method. Also comparisons are made between the Adomian decomposition method and the homotopy perturbation method. The results reveal that He’s homotopy perturbation method is very effective and simple and in these examples leads to the exact solutions.     

Fuzzy logistic regression based on the least squares approach with application in clinical studies

To model fuzzy binary observations, a new model named “Fuzzy Logistic Regression” is proposed and discussed in this study. In fact, due to the vague nature of binary observations, no probability distribution can be considered for these data. Therefore, the ordinary logistic regression may not be appropriate. This study attempts to construct a fuzzy model based on possibility of success. These possibilities are defined by some linguistic terms such as …, low, medium, high…. Then, by use of the Extension principle, the logarithm transformation of “possibilistic odds” is modeled based on a set of crisp explanatory variables observations. Also, to estimate parameters in the proposed model, the least squares method in fuzzy linear regression is used. For evaluating the model, a criterion named the “capability index” is calculated. At the end, because of widespread applications of logistic regression in clinical studies and also, the abundance of vague observations in clinical diagnosis, the suspected cases to Systematic Lupus Erythematosus (SLE) disease is modeled based on some significant risk factors to detect the application of the model. The results showed that the proposed model could be a rational substituted model of an ordinary one in modeling the clinical vague status.     

High sensitive optode for selective determination of Ni based on the covalently immobilized thionine in agarose membrane

A novel Ni²⁺ optode was prepared by covalent immobilization of thionine, 3,7-diamine-5-phenothiazoniom thionineacetate, in a transparent agarose membrane. Influences of various experimental parameters on Ni²⁺ sensing, including the reaction time, the solution pH and the concentration of reagents were investigated. Under the optimized conditions, a linear response was obtained for Ni²⁺ concentrations ranging from 1.00 × 10⁻¹⁰ to 1.00 × 10⁻⁷ mol l⁻¹ with an R² value of 0.9985. The detection limit (3σ) of the method for Ni²⁺ was 9.30 × 10⁻¹¹ mol l⁻¹. The influence of several potentially interfering ions such as Ag⁺, Hg²⁺, Cd²⁺, Zn²⁺, Pb²⁺, Cu²⁺, Mn²⁺, Co³⁺, Cr³⁺, Al³⁺ and Fe³⁺ on the determination of Ni²⁺ was studied and no significant interference was observed. The membrane showed a good durability and short response time with no evidence of reagent leaching. The membrane was successfully applied for the determination of Ni²⁺ in environmental water samples.     

A note on the stability of fractional order systems

In this paper, a new approach is suggested to investigate stability in a family of fractional order linear time invariant systems with order between 1 and 2. The proposed method relies on finding a linear ordinary system that possesses the same stability property as the fractional order system. In this way, instead of performing the stability analysis on the fractional order systems, the analysis is converted into the domain of ordinary systems which is well established and well understood. As a useful consequence, we have extended two general tests for robust stability check of ordinary systems to fractional order systems.     

Face recognition based on 3D ridge images obtained from range data

In this paper, we present an approach for 3D face recognition from frontal range data based on the ridge lines on the surface of the face. We use the principal curvature, k_max, to represent the face image as a 3D binary image called ridge image. The ridge image shows the locations of the ridge points around the important facial regions on the face (i.e., the eyes, the nose, and the mouth). We utilized the robust Hausdorff distance and the iterative closest points (ICP) for matching the ridge image of a given probe image to the ridge images of the facial images in the gallery. To evaluate the performance of our approach for 3D face recognition, we performed experiments on GavabDB face database (a small size database) and Face Recognition Grand Challenge V2.0 (a large size database). The results of the experiments show that the ridge lines have great capability for 3D face recognition. In addition, we found that as long as the size of the database is small, the performance of the ICP-based matching and the robust Hausdorff matching are comparable. But, when the size of the database increases, ICP-based matching outperforms the robust Hausdorff matching technique.     

Constrained Motif Discovery in Time Series

The goal of motif discovery algorithms is to efficiently find unknown recurring patterns. In this paper, we focus on motif discovery in time series. Most available algorithms cannot utilize domain knowledge in any way which results in quadratic or at least super-linear time and space complexity. In this paper we define the Constrained Motif Discovery problem which enables utilization of domain knowledge into the motif discovery process. The paper then provides two algorithms called MCFull and MCInc for efficiently solving the constrained motif discovery problem. We also show that most unconstrained motif discovery problems be converted into constrained ones using a change-point detection algorithm. A novel change-point detection algorithm called the Robust Singular Spectrum Transform (RSST) is then introduced and compared to traditional Singular Spectrum Transform using synthetic and real-world data sets. The results show that RSST achieves higher specificity and is more adequate for finding constraints to convert unconstrained motif discovery problems to constrained ones that can be solved using MCFull and MCInc. We then compare the combination of RSST and MCFull or MCInc with two state-of-the-art motif discovery algorithms on a large set of synthetic time series. The results show that the proposed algorithms provided four to ten folds increase in speed compared the unconstrained motif discovery algorithms studied without any loss of accuracy. RSST+MCFull is then used in a real world human-robot interaction experiment to enable the robot to learn free hand gestures, actions, and their associations by watching humans and other robots interacting.     

Active Grid Information Server for grid computing

We describe the Active Grid Information Server for performing expressive resource discovery searches and resource management in a grid environment. We represent server meta-data, such as their CPU power, storage capacity and bandwidth as points in a multi-dimensional space and then express queries as predicates over these points. The design of the system is driven by its application as a part of the information infrastructure for computational grids. Such grids provide an infrastructure for sharing computing resources; an information infrastructure is their inherent part which collects resource data and provides search functionality. Our approach complements current solutions such as MDS by adding Event Condition Action rules and an ability to efficiently handle dynamic attributes. An Event Condition Action rule-based system can support ad hoc, adaptive, flexible, and dynamic schedulers that are modifiable at runtime. We evaluate our Active Grid Information Server by evaluating Event Condition Action rules for termination, confluence, and conflict.