Sharif Digital Repository

This paper presents an effective hybrid optimal modulation technique for a cascaded H-bridge (CHB) multilevel inverter. The introduced method is generalized optimal pulsewidth modulation, which can be extended to any m-level CHB multilevel inverter operating at any switching frequency. The presented hybrid optimal modulation strategy includes the pattern exchange and optimal modulation techniques. In the optimal modulation technique, the hybrid interior-point algorithm, which is more flexible and efficient than conventional optimization techniques, is applied. The proposed hybrid optimal modulation strategy yields balanced units' output fundamental voltages, and low total harmonic distortion... 

With the rapid progression of bionanorobotics, manipulation of nano-scale biosamples is becoming increasingly attractive for different biological purposes. Nevertheless, the interaction between a robotic probe and a biological sample is poorly understood and the conditions for appropriate handling is not well-known. Here, we use the molecular dynamics (MD) simulation method to investigate the manipulation process when a nanoprobe tries to move a biosample on a substrate. For this purpose, we have used Ubiquitin (UBQ) as the biomolecule, a single-walled carbon nanotube (SWCNT) as the manipulation probe, and a double-layered graphene sheets as the substrate. A series of simulations were... 

Human Brain is one of the most wonderful and complex systems which is designed for ever; A huge complex network composed of neurons as tiny biological and chemical processors which are distributed and work together as a super parallel system to do control and vital activities of human body. Brain learning simulation and hardware implementation is one of the most interesting research areas in order to make artificial brain. One of the researches in this area is Active Learning Method in brief ALM. ALM is an adaptive recursive fuzzy learning algorithm based on brain functionality and specification which models a complex Multi Input Multi Output System as a fuzzy combination of Single Input... 

Gold nanoparticles supported on poly ionic-liquid magnetic nanoparticles (MNP@PIL@Au) were synthesized by reduction of HAuCl4 with sodium borohydride. The synthesized catalyst was characterized using by AAS, TEM, FT-IR, EDS, TGA and XRD techniques. The performance of the synthesized catalyst was investigated in the reduction of nitroarenes with NaBH4. The reaction was carried out for various nitroarenes in water and mild conditions with high yields. The catalyst selectivity for the reduction of nitro group in the presences of other functional groups such as halides and alkynes was fairly well. The recycling of the catalyst was done 8 times without any significant loss of its catalytic... 

A new heterogeneous copper catalyst was synthesized by immobilization of copper ions onto magnetic nanoparticles with a new ligand based on triazole. The catalyst was characterized using scanning and transmission electron microscopies, atomic absorption and Fourier transform infrared spectroscopies, and thermogravimetric, elemental and energy-dispersive X-ray analyses. The results confirmed that a good level of organic groups was immobilized on the magnetic nanoparticles. Huisgen cycloaddition reaction was chosen as a model reaction for the investigation of catalyst activity under green conditions. Phenylacetylene and benzyl bromide derivatives were used for the synthesis of triazoles. The... 

We investigate the parallel partially cooperative relay broadcast channel with unmatched degraded subchannels. We characterize the capacity region of this channel in the general setting that, transmitter sends a common message for both users and a private message for each of them. Our achievability result is based on the choosing appropriate parameters for the general achievable rate region of partially cooperative relay broadcast channel. We provide a converse proof for this achievable region as well, which establishes the capacity region.© 2009 IEEE  

Call admission control schemes are crucially Important concepts ID researches on cellular mobile networks. One of them is the new thinning scheme. In this study we define a confidence interval for threshold and the numerical results show the performance of the scheme in defined interval. Also we discuss the sensitivity of the scheme (in sample-based sensitivity method) at different thresholds. The results show the better performance in confidence thresholds and sensitivity of scheme into variations of thresholds  

Measuring centrality in a social network, especially in bipartite mode, poses many challenges, for example, the requirement of full knowledge of the network topology, and the lack of properly detecting top-kbehavioral representative users. To overcome the above mentioned challenges, we propose HellRank, an accurate centrality measure for identifying central nodes in bipartite social networks. HellRank is based on the Hellinger distance between two nodes on the same side of a bipartite network. We theoretically analyze the impact of this distance on a bipartite network and find upper and lower bounds for it. The computation of the HellRank centrality measure can be distributed, by letting... 

Recommendation plays an increasingly important role in our daily lives. Recommender systems automatically suggest items to users that might be interesting for them. Recent studies illustrate that incorporating social trust in Matrix Factorization methods demonstrably improves accuracy of rating prediction. Such approaches mainly use the trust scores explicitly expressed by users. However, it is often challenging to have users provide explicit trust scores of each other. There exist quite a few works, which propose Trust Metrics to compute and predict trust scores between users based on their interactions. In this paper, first we present how social relation can be extracted from users'... 

Recommendation plays an increasingly important role in our daily lives. Recommender systems automatically suggest items to users that might be interesting for them. Recent studies illustrate that incorporating social trust in Matrix Factorization methods demonstrably improves accuracy of rating prediction. Such approaches mainly use the trust scores explicitly expressed by users. However, it is often challenging to have users provide explicit trust scores of each other. There exist quite a few works, which propose Trust Metrics to compute and predict trust scores between users based on their interactions. In this paper, first we present how social relation can be extracted from users'... 

Human mind from system perspective deals with high dimensional complex world as an adaptive Multi-Input Multi-Output complex system. This view is theorized by reductionism theory in philosophy of mind, where the world is represented as logical combination of simpler sub-systems for human so that operate with less energy. On the other hand, Human usually uses linguistic rules to describe and manipulate his expert knowledge about the world; the way that is well modeled by Fuzzy Logic. But how such a symbolic form of knowledge can be encoded and stored in plausible neural circuitry? Based on mentioned postulates, we have proposed an adaptive Neuro-Fuzzy machine in order to model a rule-based... 

In this paper a new probabilistic view to the Visual Secret Sharing (VSS) is presented and as a result, a new VSS scheme is proposed. The proposed scheme is a threshold scheme with perfect black pixel reconstruction. We present the scheme by introducing two sets C0 and C1. We consider an unknown probability distribution on the elements of C0 and C 1. Perfectness provides some linear constraints on the probabilities of C0 and C1 elements. By solving the provided system of linear equations, the probability distributions are specified and we achieve a perfect VSS scheme. ©2008 IEEE  

In systems with hysteresis behavior like magnetic cores, Piezo actuators, Shape Memory Alloy(SMA), we essentially need an accurate modeling of hysteresis either for design or performance evaluation; also in some control applications accurate system identification is needed. One of the famous methods of Hysteresis modeling is Preisach model. In this numerical method hysteresis is modeled by linear combination of smaller hysteresis loops as an elemental operator and local memory. In this paper we discuss those Radial Base artificial neural networks (RBF) which provides natural settings in accordance with the Preisach model. It is shown that the proposed approach can represent hysteresis... 

Active Learning Method (ALM) is a powerful fuzzy soft computing tool, developed originally in order to promote an engineering realization of human brain. This algorithm, as a macro-level brain imitation, has been inspired by some behavioral specifications of human brain and active learning ability. ALM is an adaptive recursive fuzzy learning algorithm, in which a complex Multi Input, Multi Output system can be represented as a fuzzy combination of several Single-Input, Single-Output systems. SISO systems as associative layer of algorithm capture partial spatial knowledge of sample data space, and enable a granular knowledge resolution tuning mechanism through the learning process. The... 

In some systems with hysteresis behavior like Shape Memory Alloy (SMA) actuators and Piezo actuators, we essentially need an accurate modeling of hysteresis either for controller design or performance evaluation. One of the most interesting Hysteresis non-linearity identification methods is Preisach model in which hysteresis is modeled by linear combination of elemental operators. Despite good ability of Preisach modeling to extract main features of system with hysteresis behavior, cause of tough numerical nature of Preisach, it is not convenient to use in real-time control applications. In this paper we present a novel method based on Artificial Neural Network. For evaluation of proposed... 

In this paper an extension of the sparse decomposition problem is considered and an algorithm for solving it is presented. In this extension, it is known that one of the shifted versions of a signal s (not necessarily the original signal itself) has a sparse representation on an overcomplete dictionary, and we are looking for the sparsest representation among the representations of all the shifted versions of s. Then, the proposed algorithm finds simultaneously the amount of the required shift, and the sparse representation. Experimental results emphasize on the performance of our algorithm. ©2008 IEEE