Sharif Digital Repository

Peer-to-Peer (P2P) cloud systems are becoming more popular due to the high computational capability, scalability, reliability, and efficient data sharing. However, sending and receiving a massive amount of data causes huge network traffic leading to significant communication delays. In P2P systems, a considerable amount of the mentioned traffic and delay is owing to the mismatch between the physical layer and the overlay layer, which is referred to as locality problem. To achieve higher performance and consequently resilience to failures, each peer has to make connections to geographically closer peers. To the best of our knowledge, locality problem is not considered in any well known P2P... 

Considering the globalization concept, all companies around the world are able to communicate with each other, share their technologies, and eliminate their production incapability issues. To achieve this goal, Cloud manufacturing system has been proposed. One of the most important issues in Cloud manufacturing is service composition, in which composition of cloud services is determined to meet customer orders. This paper considers a single-product supply network over one period in Cloud manufacturing system with the goal of service composition optimization. The network is designed in a layered structure considering transportation and production services and customers can be classified in... 

In this paper, we study the secrecy throughput of a full-duplex wireless powered communication network (WPCN) for Internet of Things (IoT). The WPCN consists of a full-duplex multiantenna base station (BS) and a number of sensor nodes. The BS transmits energy all the time, and each node harvests energy prior to its transmission time slot. The nodes sequentially transmit their confidential information to the BS, and the other nodes are considered as potential eavesdroppers. We first aim to optimize the sum secrecy throughput of the nodes. The optimization variables are the duration of the time slots and the BS beamforming vectors in different time slots. The optimization problem is shown to... 

We investigate the potentials of applying the coded caching paradigm in wireless networks. In order to do this, we investigate physical layer schemes for downlink transmission from a multiantenna transmitter to several cache-enabled users. As the baseline scheme, we consider employing coded caching on the top of max-min fair multicasting, which is shown to be far from optimal at high-SNR values. Our first proposed scheme, which is near-optimal in terms of DoF, is the natural extension of multiserver coded caching to Gaussian channels. As we demonstrate, its finite SNR performance is not satisfactory, and thus we propose a new scheme in which the linear combination of messages is implemented... 

Enabling cooperation in a NOMA system is a promising approach to improve its performance. In this paper, we study the cooperation in a secure NOMA system, where the legitimate users are distributed uniformly in the network and the eavesdroppers are distributed according to a homogeneous Poisson point process. We consider a cooperative NOMA scheme (two users are paired as strong and weak users) in two phases: 1) Direct transmission phase, in which the base station broadcasts a superposition of the messages, 2) Cooperation phase, in which the strong user acts as a relay to help in forwarding the messages of the weak user. We study the secrecy outage performance in two cases: (i) security of... 

In this paper, we investigate the problem of secure communication with covert requirement in untrusted relaying networks. Our considered system model contains one source, one destination, one untrusted relay, and one Willie. The untrusted relay tries to extract the information signal, while the goal of Willie is to detect the presence of the information signal transmitted by the source, in the current time slot. To overcome these two attacks, it is assumed that the destination and the source inject jamming signal to the network in phase I and phase II, respectively. Accordingly, the communication in our proposed system model is accomplished in two phases. In the first phase, when the source... 

In this paper, a novel method for capturing the information of a powerful and trained deep convolutional neural network and distilling it into a training smaller network is proposed. This is the first time that a saliency map method is employed to extract useful knowledge from a convolutional neural network for distillation. This method, despite of many others which work on final layers, can successfully extract suitable information for distillation from intermediate layers of a network by making class specific attention maps and then forcing the student network to mimic producing those attentions. This novel knowledge distillation training is implemented using state-of-the-art DeepLab and... 

The belief propagation algorithm is a state of the art decoding technique for a variety of linear codes such as LDPC codes. The iterative structure of this algorithm is reminiscent of a neural network with multiple layers. Indeed, this similarity has been recently exploited to improve the decoding performance by tuning the weights of the equivalent neural network. In this paper, we introduce a new network architecture by increasing the number of variable-node layers, while keeping the check-node layers unchanged. The changes are applied in a manner that the decoding performance of the network becomes independent of the transmitted codeword; hence, a training stage with only the all-zero... 

Regarding increasing popularity of Ad hoc networks, the routing protocols employed in these networks should be validated before deployment. Formal methods are used nowadays to find defects in protocols specification. In this paper, we explain different methods of formal modeling and verification of routing protocols of ad hoc networks. We derive the key concepts that are vital in modeling ad hoc network protocols and then modify process algebra, appropriate for verifying protocols at network layer. This process algebra implements mobility behaviors of underlying infrastructure implicitly in the semantics of broadcasting. The semantics of broadcast communication also abstracts away the... 

In this paper, a cross-layer approach introducing new design parameters is developed for multi-rate wireless links. The novel design jointly exploits the finite-length queuing at the data link layer and the adaptation capability of the adaptive modulation and coding (AMC) scheme at the physical layer to optimize system performance for a wireless link. The analytical framework is based on discrete time Markov Chain. The performance metrics such as packet drop probability, channel packet error rate and packet loss rate are derived. Using these metrics, a constrained optimization problem is solved numerically to maximize the overall system throughput  

According to the fact that humans and animals show marvelous abilities in walking on irregular terrain, there is a strong need for adaptive algorithms in walking of biped robots to behave like them. Since the stance leg can easily rise from the ground and it can easily rotate about the toe or the heel, the problem of controlling the biped robots is difficult. In this paper, according to the adaptive locomotion patterns of animals, coordination and control of body links have been done with Central Pattern Generator (CPG) in spinal cord and feedback network from musculoskeletal system. A one layer feedforward neural network that its inputs are the scaled joint variables and the touch sensors... 

As multi-hop wireless networks are attracting more attention, the need to evaluate their performance becomes essential. In order to evaluate the performance metrics of multi-hop wireless networks, including sending and receiving rates of a node as well as the collision probability, a model based on Stochastic Reward Nets (SRNs) is proposed. The proposed SRN models a typical node in such networks, considered as a general template to be applied to any wireless node. The SRN model of a single node is designed to take transmission effects of all neighboring nodes into account, while ignoring the ones whose transmission has no effect on the node under-study. Applying the proposed SRN to each node... 

This article is the first attempt to employ deep learning to estimate the frequency performance of the rotating multi-layer nanodisks. The optimum values of the parameters involved in the mechanism of the fully connected neural network are determined through the momentum-based optimizer. The strength of the method applied in this survey comes from the high accuracy besides lower epochs needed to train the multi-layered network. It should be mentioned that the current nanostructure is modeled as a nanodisk on the viscoelastic substrate. Due to rotation, the centrifugal and Coriolis effects are considered. Hamilton’s principle and generalized differential quadrature method (GDQM) are presented... 

This article is the first attempt to employ deep learning to estimate the frequency performance of the rotating multi-layer nanodisks. The optimum values of the parameters involved in the mechanism of the fully connected neural network are determined through the momentum-based optimizer. The strength of the method applied in this survey comes from the high accuracy besides lower epochs needed to train the multi-layered network. It should be mentioned that the current nanostructure is modeled as a nanodisk on the viscoelastic substrate. Due to rotation, the centrifugal and Coriolis effects are considered. Hamilton’s principle and generalized differential quadrature method (GDQM) are presented... 

Physics-informed neural networks (PINNs) have received significant attention as a unified framework for forward, inverse, and surrogate modeling of problems governed by partial differential equations (PDEs). Training PINNs for forward problems, however, pose significant challenges, mainly because of the complex non-convex and multi-objective loss function. In this work, we present a PINN approach to solving the equations of coupled flow and deformation in porous media for both single-phase and multiphase flow. To this end, we construct the solution space using multi-layer neural networks. Due to the dynamics of the problem, we find that incorporating multiple differential relations into the... 

Hand pose estimation is one of the most attractive research areas for image processing. Among the human body parts, hands are particularly important for human–machine interactions. The advent of commercial depth cameras along with the rapid growth of deep learning has made great progress in all image processing fields, especially in hand pose estimation. In this study, using depth data, we introduce two hybrid deep neural networks to estimate 3D hand poses with fewer computations and higher accuracy compared with their counterparts. Due to the fact that the dimensions of data are reduced while passing through successive layers of networks, which causes data to be lost, we use the concept of... 

Absorption of carbon dioxide (CO2) in aqueous solutions can be improved by the addition of other compounds. However, this requires a large amount of equilibrium data for solubility estimation in a wide ranges of temperature, pressure and concentration. In this paper, a model based on an artificial neural network (ANN) was proposed and developed with mixtures containing monoethanolamine (MEA), diethanolamine (DEA), methyldiethanolamine (MDEA), 2-amino-2-methyl-1-propanol (AMP), methanol, triethanolamine (TEA), piperazine (PZ), diisopropanolamine (DIPA) and tetramethylensulfone (TMS) to predict solubility of CO2 in mixed aqueous solution (especially in binary and ternary mixtures) over wide... 

We provide a framework for modeling and analyzing both qualitative and quantitative aspects of mobile ad hoc network (MANET) protocols above the data-link layer. We extend Restricted Broadcast Process Theory [11,9]: delay functions are assigned to actions, while the semantics captures the interplay of a MANET protocol with stochastic behavior of the data-link and physical layer, and the dynamic topology. A continuous-time Markov chain is derived from our semantic model by resolving non-determinism, using the notion of weak Markovian network bisimilarity. The framework is applied to a leader election algorithm  

In this paper, a low energy yet reliable communication scheme for network-on-chip is suggested. To reduce the communication energy consumption, we invoke low-swing signals for transmitting data, as well as data encoding techniques, for minimizing both self and coupling switching capacitance activity factors. To maintain the communication reliability of communication at low-voltage swing, an error control coding (ECC) technique is exploited. The decision about end-To-end or hop-To-hop ECC schemes and the proper number of detectable errors are determined through high-level mathematical analysis on the energy and reliability characteristics of the techniques. Based on the analysis, the extended...