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Double-profile intersection (DoPIo) ultrasound is a new acoustic radiation force (ARF) imaging technique that exploits scatterer shearing under two different tracking point spread functions to estimate shear elastic moduli. Previous versions of DoPIo used an empirically derived model to relate the time where two displacement profiles, each measuring displacement in identical positions using different aperture sizes. We herein propose an alternative, machine learning-based approach that obviates the need for an empirically-derived model by replacing it with a support vector machine that uses paired displacement profiles in their entirety. In this work, we describe the implementation of such a model, validate its ability in shear modulus for simulated materials, and deploy it to image a calibrated phantom.

Despite its several qualities, the Poggio-Miller-Chang-Harrington-Wu-Tsai (PMCHWT) formulation for simulating scattering by dielectric media suffers from numerical instabilities and severe ill-conditioning at low frequencies. While this drawback has been the object of numerous solution attempts in the standard low-frequency breakdown regime for scattering problems, the eddy-current regime requires a specific treatment. In this contribution, we present an extension of the recently introduced quasi-Helmholtz projectors based preconditioning of the PMCHWT to obtain an equation stable at low frequencies and specifically in three regimes relevant for eddy currents analyses: (i) when the frequency decreases with a constant conductivity, (ii) when the conductivity increases at fixed frequency and (iii) when the frequency decreases while keeping the product of the frequency and the conductivity constant. Being based on quasi-Helmholtz projectors our new strategy does not further degrade the conditioning of the original equation and is compatible with existing fast solvers. The resulting full-wave formulation is capable of smoothly transitioning from simulations at high frequencies to the different low-frequency regimes which encompass, in particular, eddy currents applications. Numerical results demonstrate the validity of our approach in all the regimes with a special emphasis given to eddy currents.

We live in world where software is everywhere, it is a part of everything we do. By using software our daily work becomes easier and our lives become simpler. Many software development methodologies exist today and determining which methodology to use in a software project becomes one of the challenges faced by software engineers. In this paper, we define a big number of criteria which can characterize a software project in all its aspects including project features, development team, software characteristics, development tools, requirement, business model and provided documentation. Then, we present the mathematical formalization of our proposed selecting method based on tree decision. The aim of this method is to select the right software mythology for a given software project starting from the values of the defined criteria. Finally, we illustrate the developed software tool called SDMSelector which provides to a project manager the right methodology to be followed in his development project.

Voltage stability is an important factor, which needs to be taken into consideration in order to avoid voltage collapse. There are different methods used to study the voltage collapse phenomenon. In this paper, V-Q Sensitivity and Modal analyses methods are implemented on IEEE 14 bus system. Modal analysis method makes use the Jacobian matrix to determine the eigenvalues necessary to evaluate the voltage stability and V-Q Sensitivity method makes use of the diagonal elements of the inverse of reduced Jacobian matrix to a assess this stability. In this paper, our main objective is to apply V-Q Sensitivity and Modal analysis methods by analyzing the effect of neglecting the weak coupling between reactive power Q and voltage angle δ A comparison in terms of the weakest buses is made between the results obtained by taking into account the weak coupling between Q and δ, i.e., by considering the reduced Jacobian matrix and those obtained by neglecting this coupling.

Transient stability analysis is an indispensible part of utility system planning. At present, it still relies on the classical numerical integration methods which are accurate but time consuming for large power systems. This paper proposes Gear's method and the block method to reduce the computational cost. These methods are used to simulate the IEEE 39 Bus system. The classical model representation of the power system is used here. To verify their effectiveness a comparison to Runge-Kutta 4th order method, which is one of the most used methods to solve the problem is done. The Gear and block methods are found to be very accurate with less computational time than the classical Runge-kutta method. The results obtained are conclusive and satisfying.

The Electroencephalography (EEG) forward problem is of key importance for EEG-based neuroimaging. However, it is traditionally plagued by computational inefficiencies every time the subject model's skin-to-skull conductivity ratio is high. In this paper, we present a new integral equation approach to solve the EEG forward problem which delivers computationally stable results independently of the conductivity ratio. This is obtained by leveraging on a mixed conforming discretization together with a suitably designed problem splitting scheme. Numerical results corroborate the analysis and demonstrate the effectiveness of the technique.

Delay Tolerant Network (DTN) has the potential to connect nodes in an environment where an end-to-end path may not be available between source and destination. Resources constraint is among the most serious issues in DTN. In this paper, we consider the scenario where we want to transmit an information (message) from a source to a group of people in the network area. Using the epidemic routing protocol the message can reach its destinations as fast as possible, but the protocol consumes too much resources. We model the messages propagation under the epidemic routing protocol by an Ordinary Differential Equation (ODE). And, we derive the optimal retention of a message by a node, taking into account that all nodes are infected. Simulations results show that, using the proposed optimal control in epidemic routing context, we can reach the same performances of epidemic routing while minimizing the resource consumption.

The mobile nodes in delay tolerant networks communicate with each other using a store-carry and forward mechanism even though there is no permanent link between the source and the destination. This mechanism requires cooperation between nodes. However, because of limited resources such as storage and energy, most of the nodes act in a non-cooperative way. In this paper, an incentive mechanism is proposed to encourage messages caching in DTNs. The source offers a reward to the relays to incite them to participate in delivering the message to the destination. We model the interaction between the source and the relays as a Stackelberg game in which the source takes the role of the leader and the relays respond based on the leader's move. Also, two different learning algorithms are presented: one for the leader, the other for the followers, to allow them to take the best decision and finally, to converge to the Stackelberg Equilibrium.

When planning the delivery in a supply chain, it is necessary to prepare a schedule of deliveries for different products at different sites during the planning horizon while satisfying demand and respecting material and financial resources constraints. In addition, to manage a supply chain, we need to solve problems such as the JRP (Joint Replenishment Problem) taking into account the problem of organizing delivery tours as in the IRP (Inventory Routing Problem) model. In this study, the conventional assumption of periodic deliveries for each pair (product, website) was lifted to better utilize the degrees of freedom in the delivery program over a common cycle used in the optimization as a planning horizon. Assuming that the planning horizon was previously determined, the proposed formulation for the joint delivery model (JDP) takes the form of a multi-site version of the classical multi-product IRP problem, but with major and minor set-up costs and with consideration of an indirect grouping strategy for deliveries for each This is a mixed integer linear programming problem (MILP) having real variables and binary variables. As such, it is known as a NP-hard problem.

Despite its attractive benefits, cloud adoption is challenged by some criteria of security and privacy. Access Control is one of the traditional and essential security tools of data protection. The decision to grant access to a resource must ensure secure management with a specific attention to privacy and data protection regulations. In particular, the challenge is more important with public clouds as many governing authorities could be involved in one cloud scenario. This implies a difficulty to work out which regulation should be applicable in case of conflict. In recent years, many access control models were proposed. Despite increasing legislative pressure, few of these propositions take care of privacy requirements in their security policies specification and enforcement. In this paper, we propose to enforce privacy compliance in access control policies for the context of public cloud. Throughout the use of ontology tools, we propose an approach for checking privacy enforcement with access control conditions. We also suggest the use of privacy safeguards notification where the threat to privacy protection is related to the secondary usage of personal data more than just the data access itself.

Using Delay Tolerant Networks (DTNs) would facilitate the connection of devices and areas across the world that are under-served by current networks. The DTNs are based on the concept of store-carry-and-forward protocols. A node may store a message in its buffer and carry it for a short or a long period of time, until an appropriate forwarding opportunity arises. A critical challenge is to determine routes through the network without even having an end-to-end connection. In order to increase the probability of message delivery, a known approach is implemented using epidemic message replication. This combination of long-term storage and message replication imposes a high storage and bandwidth overhead. Thus, efficient scheduling and dropping policies are necessary to decide which messages should be discarded when nodes' buffers operate close to their capacity. If a relay buffer is full and needs to store a new packet, it has to decide either to keep the current message or to drop it. This decision depends on the number of transmissions of the message in its buffer and the message which has just arrived. In this paper, a Partially Observed Markov Decision Process (POMDP) framework is proposed to solve the problem of buffer management in DTNs. This modeling technique predicts some properties of the optimal buffer management policy. In addition, numerical examples are presented to illustrate the findings.

A Delay Tolerant Networks is a network where contemporaneous connectivity among all nodes does not exist. Conventional routing protocols are not suitable for DTN because they assume end-to-end connectivity. DTNs are characterized by the absence of it. This leads to the critical problem of how to route a packet from one node to another, in such a network. This problem becomes more complex, when the node mobility also is considered. In this paper, a novel strategy is presented and evaluated for controlling messages in DTNs based on the delivery predictability metric of probabilistic routing protocols. Simulation results show that this new strategy gives better results in terms of delivered and relayed messages.