Sharif Digital Repository

The uncertainty principle sets a lower bound on the uncertainties of two incompatible observables measured on a particle. The uncertainty lower bound can be reduced by considering a particle as a quantum memory entangled with the measured particle. In this paper, we consider a tripartite scenario in which a quantum state has been shared among Alice, Bob, and Charlie. The aim of Bob and Charlie is to minimize Charlie's lower bound about Alice's measurement outcomes. To this aim, they concentrate their correlation with Alice in Charlie's side via a cooperative strategy based on local operations and classical communication. We obtain a lower bound for Charlie's uncertainty about Alice's... 

Portfolio theory has found its model in numerous engineering applications for optimizing the electrical generation mix of an electricity area. However, to have better performance of this theory, this paper presents a new heuristic method as known modified artificial bee colony (MABC) to portfolio optimization problem. Moreover, we consider both dis-patchable and non-dis-patchable constrains variables and energy sources. Note that the proposed MABC method uses a Chaotic Local Search (CLS) to enhance the self searching ability of the original ABC algorithm. Resulting, in this paper a portfolio theory-based MABC model that explicitly distinguishes between electricity generation (energy),... 

The complex structure of the power system and the pivotal role of electrical energy in determining the socio-economic indicators of any countries lead the policy makers of power industry to take into account the expansion planning of generation system with high priority. Considering the intense fluctuations of costs in electrical energy generation (particularly due to variation of fuel prices within the recent years in the Middle East), finding an optimal generation portfolio, regardless of the costs variations risk looks impossible. The portfolio theory, as an efficient tool for risk management, provides a proper solution to materialize the optimal generation portfolios with the following... 

The amount of high dimensional data produced by visual sensors in the smart environments and by autonomous vehicles is increasing exponentially. In order to search and model this data for real-time applications, the dimensionality of the data should be reduced. In this paper, a novel dimensionality reduction algorithm based on fuzzy rough set theory, called Centralized Binary Mapping (CBM), is proposed. The fuzzy CBM kernel is used for extracting the central elements and the memory cells from the blocks of high dimensional data. The proposed applications of CBM in this paper include hashing and generative modelling of multimedia big data. The robustness of the proposed CBM based hashing... 

Currently, diagnosis of skin diseases is based primarily on the visual pattern recognition skills and expertise of the physician observing the lesion. Even though dermatologists are trained to recognize patterns of morphology, it is still a subjective visual assessment. Tools for automated pattern recognition can provide objective information to support clinical decision-making. Noninvasive skin imaging techniques provide complementary information to the clinician. In recent years, optical coherence tomography (OCT) has become a powerful skin imaging technique. According to specific functional needs, skin architecture varies across different parts of the body, as do the textural... 

OCT skin images suffer from artifacts. Speckle is the main artifact while the other one is called background noise. In this study, we propose an algorithm that significantly reduces the background noise before applying a speckle reduction method. The results show that the diagnostically relevant features in the images become clearer after applying the proposed method. We used sub-pixel weighted median filtering for speckle reduction. The results from background noise removal in combination with the proposed speckle reduction algorithm show a significant improvement in the clarity of diagnostically relevant features in in-vivo human skin images. © 2017 SPIE  

The increasing demand on execution of large-scale Cloud workflow applications which need a robust and elastic computing infrastructure usually lead to the use of high-performance Grid computing clusters. As the owners of Cloud applications expect to fulfill the requested Quality of Services (QoS) by the Grid environment, an adaptive scheduling mechanism is needed which enables to distribute a large number of related tasks with different computational and communication demands on multi-cluster Grid computing environments. Addressing the problem of scheduling large-scale Cloud workflow applications onto multi-cluster Grid environment regarding the QoS constraints declared by application's... 

This paper presents a new negotiation model for designing Market- and Behavior-driven Negotiation Agents (MBDNAs) that address computational grid resource allocation problem. To determine the amount of concession for each trading cycle, the MBDNAs are guided by six factors: (1) number of negotiator's trading partners, (2) number of negotiator's competitors, (3) negotiator's time preference, (4) flexibility in negotiator's trading partner's proposal, (5) negotiator's proposal deviation from the average of its trading partners proposals, and (6) previous concession behavior of negotiator's trading partner. In our experiments, we compare grid resource consumer (GRC) of type MBDNAs (respectively... 

Providing an efficient resource allocation mechanism is a challenge to computational grid due to large-scale resource sharing and the fact that Grid Resource Owners (GROs) and Grid Resource Consumers (GRCs) may have different goals, policies, and preferences. In a real world market, various economic models exist for setting the price of grid resources, based on supply-and-demand and their value to the consumers. In this paper, we discuss the use of multiagent-based negotiation model for interaction between GROs and GRCs. For realizing this approach, we designed the Market- and Behavior-driven Negotiation Agents (MBDNAs). Negotiation strategies that adopt MBDNAs take into account the... 

Optical Coherence Optical coherence tomography is a powerful high-resolution imaging method with a broad biomedical application. Nonetheless, OCT images suffer from a multiplicative artefacts so-called speckle, a result of coherent imaging of system. Digital filters become ubiquitous means for speckle reduction. Addressing the fact that there still a room for despeckling in OCT, we proposed an intelligent speckle reduction framework based on OCT tissue morphological, textural and optical features that through a trained network selects the winner filter in which adaptively suppress the speckle noise while preserve structural information of OCT signal. These parameters are calculated for... 

Among polymers, polyaniline (PANi) has been introduced as a good candidate for muscle regeneration due to high conductivity and also biocompatibility. Herein, for the first time, we report the use of electrospun nanofibrous membrane of PAN-PANi as efficient scaffold for muscle regeneration. The prepared PAN-PANi electrospun nanofibrous membrane was characterized by scanning electron microscopy (SEM), Attenuated total reflectance fourier transform infrared spectroscopy (ATR-FTIR) and tensile examination. The softer scaffolds of non-composite electrospun nanofibrous PAN govern a higher rate of cell growth in spite of lower differentiation value. On the other hand, PAN-PANi electrospun... 

Learning a foreign language encompasses a process of trial and error that can lead to the progression of the learner's interlanguage, meaning that the learners make use of their language knowledge that receives feedback and brings about progress through diagnosis of the problems and being corrected by the teacher. Therefore, there exists a need to analyze the underlying reasons causing the students to remain taciturn with a low willingness to communicate (WTC) during a language class. There has been much work done investigating the role of the learner's stable personal characteristics (traits) along with their situational changes at a specific time (states) that influence the student's... 

Using 3-D scanned data to analyze and extract pore network plays a vital role in investigation of porous media's characteristics. In this paper, a new simple method is developed to detect pores and throats for analyzing the connectivity and permeability of the network. This automated method utilizes some of the common and well-known image processing functions which are widely accessible by researchers and this has led to an easy algorithm implementation. In this method, after polishing and quality control of images, using city-block distance function and watershed segmentation algorithm, pores and throats are detected and 3-D network is produced. This method can also be applied on 2-D images... 

In this paper, we try to recognize and distinguish different groups of arrhythmia using novel features which have been obtained from the heart rate's phase space in recent years. For this purpose, we used Triangular Phase Space Mapping (TPSM) and Parabolic Phase Space Mapping (PPSM). For recognition, we used three groups of 15 subjects using the Physionet database (Arrhythmia, Congestive Heart Failure (CHF), and Atrial Fibrillation (AF)) with Normal Sinus Rhythm (NSR). The obtained features discriminate arrhythmia from NSR by p