Enhancing encoding capacity of combinational test stimulus decompressors

While scan-based compression is widely utilized in order to alleviate the test time and data volume problems,the overall compression level is dictated not only by the chain to channel ratio but also the ratio of encodable patterns.Aggressively increasing the number of scan chains in an effort to raise the compression levels may reduce the ratio of encodable patterns,degrading the overall compression level.In this paper,we present various methods to improve the ratio of encodable patterns.These methods are b...     

Thermodynamic studies on the complete phase diagram of aqueous two phase system containing polyethylene glycol dimethyl ether 2000 and di-potassium hydrogen phosphate at different temperatures

The liquid-liquid equilibrium of polyethylene glycol dimethyl ether 2000 (PEGDME₂₀₀₀)+K₂HPO₄+H₂O system has been determined experimentally at T=(298.15,303.15,308.15 and 318.15) K. The liquid-solid and complete phase diagram of this system was also obtained at T=(298.15 and 308.15) K. A nonlinear temperature dependent equation was successfully used for the correlation of the experimental binodal data. Furthermore, a temperature dependent Setschenow-type equation was successfully used for the correlation of the tie-lines of the studied system. Moreover, the effect of temperature on the binodal curves and the tie-lines for the investigated aqueous two-phase system have been studied. Also, the free energies of cloud points for this system and some previously studied systems containing PEGDME₂₀₀₀ were calculated from which it was concluded that the increase of the entropy is the driving force for formation of aqueous two-phase systems. Additionally, the calculated free energies for phase separation of the studied systems were used to investigate the salting-out ability of the salts having different anions. Furthermore, the complete phase diagram of the investigated system was compared with the corresponding phase diagrams of previously studied systems, in which the PEGDME₂₀₀₀ has been used, in order to obtain some information regarding the phase behavior of these PEGDME₂₀₀₀+salt+water systems.     

Iterative methods for solving extended general mixed variational inequalities

In this paper, we introduce and consider a new class of mixed variational inequalities involving four operators, which are called extended general mixed variational inequalities. Using the resolvent operator technique, we establish the equivalence between the extended general mixed variational inequalities and fixed point problems as well as resolvent equations. We use this alternative equivalent formulation to suggest and analyze some iterative methods for solving general mixed variational inequalities. We study the convergence criteria for the suggested iterative methods under suitable conditions. Our methods of proof are very simple as compared with other techniques. The results proved in this paper may be viewed as refinements and important generalizations of the previous known results.     

On a new class of analytic functions associated with conic domain

The aim of this paper is to generalize the conic domain defined by Kanas and Wisniowska, and define the class of functions which map the open unit disk E onto this generalized conic domain. A brief comparison between these conic domains is the main motivation of this paper. A correction is made in selecting the range interval of order of conic domain.     

Using cross-layer metrics to improve the performance of end-to-end handover mechanisms

Network centric handover solutions for all IP wireless networks usually require modifications to network infrastructure which can stifle any potential rollout. This has led researchers to begin looking at alternative approaches. Endpoint centric handover solutions do not require network infrastructure modification, thereby alleviating a large barrier to deployment. Current endpoint centric solutions capable of meeting the delay requirements of Voice over Internet Protocol (VoIP) fail to consider the Quality of Service (QoS) that will be achieved after handoff. The main contribution of this paper is to demonstrate that QoS aware handover mechanisms which do not require network support are possible. This work proposes a Stream Control Transmission Protocol (SCTP) based handover solution for VoIP called Endpoint Centric Handover (ECHO). ECHO incorporates cross-layer metrics and the ITU-T E-Model for voice quality assessment to accurately estimate the QoS of candidate handover networks, thus facilitating a more intelligent handoff decision. An experimental testbed was developed to analyse the performance of the ECHO scheme. Results are presented showing both the accuracy of ECHO at estimating the QoS and that the addition of the QoS capabilities significantly improves the handover decisions that are made.     

A training-free nose tip detection method from face range images

Nose tip detection in range images is a specific facial feature detection problem that is highly important for 3D face recognition. In this paper, we propose a nose tip detection method that has the following three characteristics. First, it does not require training and does not rely on any particular model. Second, it can deal with both frontal and non-frontal poses. Finally, it is quite fast, requiring only seconds to process an image of 100-200 pixels (in both x and y dimensions) with a MATLAB implementation. A complexity analysis shows that most of the computations involved in the proposed algorithm are simple. Thus, if implemented in hardware (such as a GPU implementation), the proposed method should be able to work in real time. We tested the proposed method extensively on synthetic image data rendered by a 3D head model and real data using FRGC v2.0 data set. Experimental results show that the proposed method is robust to many scenarios that are encountered in common face recognition applications (e.g., surveillance). A high detection rate of 99.43% was obtained on FRGC v2.0 data set. Furthermore, the proposed method can be used to coarsely estimate the roll, yaw, and pitch angles of the face pose.     

SimClus: an effective algorithm for clustering with a lower bound on similarity

Clustering algorithms generally accept a parameter k from the user, which determines the number of clusters sought. However, in many application domains, like document categorization, social network clustering, and frequent pattern summarization, the proper value of k is difficult to guess. An alternative clustering formulation that does not require k is to impose a lower bound on the similarity between an object and its corresponding cluster representative. Such a formulation chooses exactly one representative for every cluster and minimizes the representative count. It has many additional benefits. For instance, it supports overlapping clusters in a natural way. Moreover, for every cluster, it selects a representative object, which can be effectively used in summarization or semi-supervised classification task. In this work, we propose an algorithm, SimClus, for clustering with lower bound on similarity. It achieves a O(log n) approximation bound on the number of clusters, whereas for the best previous algorithm the bound can be as poor as O(n). Experiments on real and synthetic data sets show that our algorithm produces more than 40% fewer representative objects, yet offers the same or better clustering quality. We also propose a dynamic variant of the algorithm, which can be effectively used in an on-line setting.     

A generalized least absolute deviation method for parameter estimation of autoregressive signals

This paper proposes a generalized least absolute deviation (GLAD) method for parameter estimation of autoregressive (AR) signals under non-Gaussian noise environments. The proposed GLAD method can improve the accuracy of the estimation of the conventional least absolute deviation (LAD) method by minimizing a new cost function with parameter variables and noise error variables. Compared with second- and high-order statistical methods, the proposed GLAD method can obtain robustly an optimal AR parameter estimation without requiring the measurement noise to be Gaussian. Moreover, the proposed GLAD method can be implemented by a cooperative neural network (NN) which is shown to converge globally to the optimal AR parameter estimation within a finite time. Simulation results show that the proposed GLAD method can obtain more accurate estimates than several well-known estimation methods in the presence of different noise distributions.