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Deep neural networks suffer from significant performance deterioration when there exists distribution shift between deployment and training. Domain Generalization (DG) aims to safely transfer a model to unseen target domains by only relying on a set of source domains. Although various DG approaches have been proposed, a recent study named DomainBed (Gulrajani and Lopez-Paz, 2020), reveals that most of them do not beat simple empirical risk minimization (ERM). To this end, we propose a general framework that is orthogonal to existing DG algorithms and could improve their performance consistently. Unlike previous DG works that stake on a static source model to be hopefully a universal one, our proposed AdaODM adaptively modifies the source model at test time for different target domains. Specifically, we create multiple domain-specific classifiers upon a shared domain-generic feature extractor. The feature extractor and classifiers are trained in an adversarial way, where the feature extractor embeds the input samples into a domain-invariant space, and the multiple classifiers capture the distinct decision boundaries that each of them relates to a specific source domain. During testing, distribution differences between target and source domains could be effectively measured by leveraging prediction disagreement among source classifiers. By fine-tuning source models to minimize the disagreement at test time, target-domain features are well aligned to the invariant feature space. We verify AdaODM on two popular DG methods, namely ERM and CORAL, and four DG benchmarks, namely VLCS, PACS, OfficeHome, and TerraIncognita. The results show AdaODM stably improves the generalization capacity on unseen domains and achieves state-of-the-art performance.

In this work, we consider the problem of learning the Koopman operator for discrete-time autonomous systems. The learning problem is formulated as a generic constrained regularized empirical loss minimization in the infinite-dimensional space of linear operators. We show that a representer theorem holds for the introduced learning problem under certain but general conditions, which allows convex reformulation of the problem in a specific finite-dimensional space without any approximation and loss of precision. We discuss the inclusion of various forms of regularization and constraints in the learning problem, such as the operator norm, the Frobenius norm, the operator rank, the nuclear norm, and the stability. Subsequently, we derive the corresponding equivalent finite-dimensional problem. Furthermore, we demonstrate the connection between the proposed formulation and the extended dynamic mode decomposition. We present several numerical examples to illustrate the theoretical results and verify the performance of regularized learning of the Koopman operators., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Team Khosravi

In this paper, we describe a supervised four-dimensional (4D) light field segmentation method that uses a graph-cut algorithm. Since 4D light field data has implicit depth information and contains redundancy, it differs from simple 4D hyper-volume. In order to preserve redundancy, we define two neighboring ray types (spatial and angular) in light field data. To obtain higher segmentation accuracy, we also design a learning-based likelihood, called objectness, which utilizes appearance and disparity cues. We show the effectiveness of our method via numerical evaluation and some light field editing applications using both synthetic and real-world light fields., 2016 IEEE International Conference on Computational Photography (ICCP),Evanston, IL, USA,13-15 May 2016

This paper proposes an example-based method to increase the resolution of a low-resolution image. In the proposed method, we generate example images by a novel view synthesis technique using 3D geometry reconstruction and camera pose estimation from a video or images capturing the same scene. We then increase the resolution by minimizing an energy function by searching for the optimal example from the generated example images. The proposed method has less limitations on camera positions and geometry of the target scene than those in conventional methods. Experiments demonstrate the effectiveness of the proposed method by qualitatively comparing the results of the proposed and conventional methods., ICIP 2015 : 22th IEEE International Conference on Image Processing , Sep 27-30, 2015 , Quebec, Canada

Magnetic Particle Tracking (MPT) is a relatively new non-invasive measurement technique which is often used to study dense granular flow. Its basic principle relies on tracking the movement of a single magnetic tracer by means of measuring the magnetic field strength at a suitable distance from the tracer. By assumption of a magnetic dipole and the use of minimization techniques, both location and orientation of the tracer can be determined. MPT is therefore uniquely suited for the study of non-spherical particles. The performance of the localization is largely dependent on the signal-to-noise ratio and very often relies on nonlinear optimization techniques, as the definition of the magnetic field generated by a dipole is highly nonlinear and has five degrees of freedom. In this paper, we present a semi-algebraic solution by decoupling the estimation of the position and orientation in separate algebraic solutions. The two estimates are mutually dependent, necessitating an iterative approach between the two. The main benefits of this new approach is in the speed and robustness of the algorithm, which are much higher than for the classical constrained nonlinear optimization techniques., BT/Bioprocess Engineering

Temporal link prediction (TLP) is an inference task on dynamic graphs that predicts future topology using historical graph snapshots. Existing TLP methods are usually designed for unweighted graphs with fixed node sets. Some of them cannot be generalized to the prediction of weighted graphs with non-fixed node sets. Although several methods can still be used to predict weighted graphs, they can only derive low-quality prediction snapshots sensitive to large edge weights but fail to distinguish small and zero weights in adjacency matrices. In this study, we consider the challenging high-quality TLP on weighted dynamic graphs and propose a novel inductive dynamic embedding aggregation (IDEA) method, inspired by the high-resolution video prediction. IDEA combines conventional error minimization objectives with a scale difference minimization objective, which can generate high-quality weighted prediction snapshots, distinguishing differences among large, small, and zero weights in adjacency matrices. Since IDEA adopts an inductive dynamic embedding scheme with an attentive node aligning unit and adaptive embedding aggregation module, it can also tackle the TLP on weighted graphs even with non-fixed node sets. Experiments on datasets of various scenarios validate that IDEA can derive high-quality prediction results for weighted dynamic graphs and tackle the variation of node sets. IEEE

In this paper, we study a decentralized deep neural network (DNN) task partitioning and offloading control problem for a multi-access edge computing (MEC) system with multiple wireless devices (WDs) powered by renewable energy sources. Instead of generic computational tasks, we focus on the case when the WDs and the MEC systems are computing DNN inferencing tasks. For each WD, we build a virtual local-computing CPU workload queue and an energy queue to model the DNN computation dynamics and the battery energy dynamics, respectively. We first introduce a new Lyapunov function based on the DNN tasks'; cumulative execution latency and the total energy consumptions of the entire MEC system. Using a Lyapunov optimization approach, the DNN task partitioning and offloading control at the WDs are formulated as a Lyapunov drift minimization problem. To facilitate a distributed implementation, we exploit the linear structure of the cumulative latency and the independency of each WD's local-computing CPU rate, and we decompose the centralized Lyapunov drift minimization problem into multiple distributed subproblems, each of which is associated with a single WD. Next, we develop a parametric online learning algorithm such that each sub-Lyapunov drift minimization problem is solved locally at its associated WD. The proposed solution is completely decentralized in the sense that the sequential offloading and DNN task segmentation control at each WD is determined by its local battery energy queue state information (EQSI), the channel state information (CSI), and the DNN inferencing task workload queue state information (TQSI). Numerical results reveal that the proposed online learning algorithm can achieve significant performance gain over various state-of-the-art baselines.

Magnetic Particle Tracking (MPT) is a relatively new non-invasive measurement technique which is often used to study dense granular flow. Its basic principle relies on tracking the movement of a single magnetic tracer by means of measuring the magnetic field strength at a suitable distance from the tracer. By assumption of a magnetic dipole and the use of minimization techniques, both location and orientation of the tracer can be determined. MPT is therefore uniquely suited for the study of non-spherical particles. The performance of the localization is largely dependent on the signal-to-noise ratio and very often relies on nonlinear optimization techniques, as the definition of the magnetic field generated by a dipole is highly nonlinear and has five degrees of freedom. In this paper, we present a semi-algebraic solution by decoupling the estimation of the position and orientation in separate algebraic solutions. The two estimates are mutually dependent, necessitating an iterative approach between the two. The main benefits of this new approach is in the speed and robustness of the algorithm, which are much higher than for the classical constrained nonlinear optimization techniques.

We present a proximal augmented Lagrangian based solver for general quadratic programs (QPs), relying on semismooth Newton iterations with exact line search to solve the inner subproblems. The exact line search reduces in this case to finding the zero of a one-dimensional monotone, piecewise affine function and can be carried out very efficiently. Our algorithm requires the solution of a linear system at every iteration, but as the matrix to be factorized depends on the active constraints, efficient sparse factorization updates can be employed like in active-set methods. Both primal and dual residuals can be enforced down to strict tolerances and otherwise infeasibility can be detected from intermediate iterates. A C implementation of the proposed algorithm is tested and benchmarked against other state-of-the-art QP solvers for a large variety of problem data and shown to compare favorably against these solvers.

The objective of this research is to evaluate the optimal allocation of resources of an agroindustrial enterprise dedicated to the production of nopal in brine in three presentations, through the simplex method of linear programming using the operations research methodology. As a technological tool, the Solver complement of the Excel spreadsheet is used. The issue is relevant since, in Mexico, high mortality of companies is identified in their first years, especially in micro and small ones, while those that remain in a dynamic and competitive market manifest problems in relation to decision-making that ensure efficient allocation of resources. The main findings include the construction of a mathematical model applying the simplex method to calculate the optimal production level that minimizes production costs, its application being useful to contribute to the decision-making of entrepreneurs., O objetivo desta pesquisa é avaliar a alocação ótima de recursos de uma empresa agroindustrial dedicada à produção de nopal em salmoura em três apresentações, através do método simplex de programação linear utilizando a metodologia de pesquisa operacional. O suplemento Solver da planilha do Excel é usado como uma ferramenta tecnológica. O tema é relevante já que, no México, é identificada uma alta taxa de mortalidade de empresas em seus primeiros anos, especialmente em micro e pequenas empresas, enquanto aquelas que permanecem em um mercado dinâmico e competitivo apresentam problemas em relação à tomada de decisões para garantir uma alocação eficiente de recursos. As principais conclusões incluem a construção de um modelo matemático aplicando o método simplex para calcular o nível ideal de produção que minimiza os custos de produção, o que é útil para contribuir para o processo de tomada de decisão dos empreendedores., El objetivo de la presente investigación es evaluar la asignación óptima de los recursos de una empresa agroindustrial dedicada a producción de nopal en salmuera en tres presentaciones, a través del método simplex de programación lineal usando la metodología de investigación de operaciones. Como herramienta tecnológica se utiliza el complemento Solver de la hoja de cálculo de Excel. El tema es relevante ya que, en México, se identifica alta mortandad de empresas en sus primeros años sobre todo en las micro y pequeñas, mientras que, las que permanecen en un mercado dinámico y competitivo manifiestan problemáticas con relación a la toma de decisiones que garantice una asignación eficiente de recursos. De los principales hallazgos destaca la construcción de un modelo matemático aplicando el método simplex para calcular el nivel de producción óptimo que minimice los costos de producción, resultando útil su aplicación para contribuir en la toma de decisiones de los empresarios.

The objective of this research is to evaluate the optimal allocation of resources of an agroindustrial enterprise dedicated to the production of nopal in brine in three presentations, through the simplex method of linear programming using the operations research methodology. As a technological tool, the Solver complement of the Excel spreadsheet is used. The issue is relevant since, in Mexico, high mortality of companies is identified in their first years, especially in micro and small ones, while those that remain in a dynamic and competitive market manifest problems in relation to decision-making that ensure efficient allocation of resources. The main findings include the construction of a mathematical model applying the simplex method to calculate the optimal production level that minimizes production costs, its application being useful to contribute to the decision-making of entrepreneurs., O objetivo desta pesquisa é avaliar a alocação ótima de recursos de uma empresa agroindustrial dedicada à produção de nopal em salmoura em três apresentações, através do método simplex de programação linear utilizando a metodologia de pesquisa operacional. O suplemento Solver da planilha do Excel é usado como uma ferramenta tecnológica. O tema é relevante já que, no México, é identificada uma alta taxa de mortalidade de empresas em seus primeiros anos, especialmente em micro e pequenas empresas, enquanto aquelas que permanecem em um mercado dinâmico e competitivo apresentam problemas em relação à tomada de decisões para garantir uma alocação eficiente de recursos. As principais conclusões incluem a construção de um modelo matemático aplicando o método simplex para calcular o nível ideal de produção que minimiza os custos de produção, o que é útil para contribuir para o processo de tomada de decisão dos empreendedores., El objetivo de la presente investigación es evaluar la asignación óptima de los recursos de una empresa agroindustrial dedicada a producción de nopal en salmuera en tres presentaciones, a través del método simplex de programación lineal usando la metodología de investigación de operaciones. Como herramienta tecnológica se utiliza el complemento Solver de la hoja de cálculo de Excel. El tema es relevante ya que, en México, se identifica alta mortandad de empresas en sus primeros años sobre todo en las micro y pequeñas, mientras que, las que permanecen en un mercado dinámico y competitivo manifiestan problemáticas con relación a la toma de decisiones que garantice una asignación eficiente de recursos. De los principales hallazgos destaca la construcción de un modelo matemático aplicando el método simplex para calcular el nivel de producción óptimo que minimice los costos de producción, resultando útil su aplicación para contribuir en la toma de decisiones de los empresarios.

Following the roadmap of carbon neutrality, wireless communication systems are upgrading to use green energy that comes from renewable sources, e.g., sun, tide, and wind. Due to the volatile arrival of green energy, the on-grid energy is used as a backup for a green coordinated multiple point system. In this work, a weighted sum-rate maximization problem in the green coordinated multiple point system is investigated by expecting non-positive consumption of the on-grid energy in the long term. Motivated by the capacity-achieving property and simple implementation, an online zero-forcing dirty paper is proposed to update the precoding matrices by combining statistical learning with the Lyapunov learning technique. A tradeoff relation is theoretically established to show that the long-term weighted sum rate approaches the O(V)-neighbor of optimal value while the long-term on-grid energy increases at a rate of O(log2(V)/√V), where V is an introduced control parameter. Numerical results are used to verify the performance of the proposed online adaptive precoder. IEEE

The objective of this research is to evaluate the optimal allocation of resources of an agroindustrial enterprise dedicated to the production of nopal in brine in three presentations, through the simplex method of linear programming using the operations research methodology. As a technological tool, the Solver complement of the Excel spreadsheet is used. The issue is relevant since, in Mexico, high mortality of companies is identified in their first years, especially in micro and small ones, while those that remain in a dynamic and competitive market manifest problems in relation to decision-making that ensure efficient allocation of resources. The main findings include the construction of a mathematical model applying the simplex method to calculate the optimal production level that minimizes production costs, its application being useful to contribute to the decision-making of entrepreneurs., O objetivo desta pesquisa é avaliar a alocação ótima de recursos de uma empresa agroindustrial dedicada à produção de nopal em salmoura em três apresentações, através do método simplex de programação linear utilizando a metodologia de pesquisa operacional. O suplemento Solver da planilha do Excel é usado como uma ferramenta tecnológica. O tema é relevante já que, no México, é identificada uma alta taxa de mortalidade de empresas em seus primeiros anos, especialmente em micro e pequenas empresas, enquanto aquelas que permanecem em um mercado dinâmico e competitivo apresentam problemas em relação à tomada de decisões para garantir uma alocação eficiente de recursos. As principais conclusões incluem a construção de um modelo matemático aplicando o método simplex para calcular o nível ideal de produção que minimiza os custos de produção, o que é útil para contribuir para o processo de tomada de decisão dos empreendedores., El objetivo de la presente investigación es evaluar la asignación óptima de los recursos de una empresa agroindustrial dedicada a producción de nopal en salmuera en tres presentaciones, a través del método simplex de programación lineal usando la metodología de investigación de operaciones. Como herramienta tecnológica se utiliza el complemento Solver de la hoja de cálculo de Excel. El tema es relevante ya que, en México, se identifica alta mortandad de empresas en sus primeros años sobre todo en las micro y pequeñas, mientras que, las que permanecen en un mercado dinámico y competitivo manifiestan problemáticas con relación a la toma de decisiones que garantice una asignación eficiente de recursos. De los principales hallazgos destaca la construcción de un modelo matemático aplicando el método simplex para calcular el nivel de producción óptimo que minimice los costos de producción, resultando útil su aplicación para contribuir en la toma de decisiones de los empresarios.

The possibility of minimizing an ultrawide bandpass microstrip microwave filter by bending shortcircuited stubs is shown. Simulated case without bending and three cases with bending are presented.

An example of minimizing the width of a microwave filter structure by bending the stubs and reducing the number of required via holes is presented in this paper. It is shown that the filter area can be reduced near 2 times after bending the stubs. EM model of minimized structure is presented and compared with common filter design.

A PI design method for faulty networks is provided based on Linear Matrix Inequalities (LMIs). Feedback controllers for irrigation canals are designed based on LMIs, but sparsity constraints are also imposed to make zero the feedback control law elements not corresponding to the tuning PI parameters. Therefore, the design method is halfway between a PI controller and an optimal feedback control law, also providing stability guarantees up to a maximum probability of packet losses. The objective of the downstream controller is to maintain the water levels upstream from each downstream check structure of each canal pool, while gravity-offtake gates satisfy downstream water demands. The proposed approach is tested using the irrigation system of ASCE Test Canal 1 and compared with other tuning methods via simulation. Our results show that the design method can be a useful tool when dealing with control systems under faulty networks.

Operation research gives useful medical methods/gear employed in planning and management of agribusiness. Right from 1954 researchers commenced analyzing the applications of Operations studies in agriculture. Agriculture makes provision for food and is vital for the GDP of India with the ever-growing population. This paper makes a try to use numerous Operations research techniques inclusive of Linear Programming troubles to provide an explanation for how the combination of these strategies in agriculture will no longer only assist in reducing the triumphing problems in agriculture however additionally lead to better efficiency.

In this letter, we propose an algorithm for learning a sparse weighted graph by estimating its adjacency matrix under the assumption that the observed signals vary smoothly over the nodes of the graph. The proposed algorithm is based on the principle of majorization-minimization (MM), wherein we first obtain a tight surrogate function for the graph learning objective and then solve the resultant surrogate problem which has a simple closed form solution. The proposed algorithm does not require tuning of any hyperparameter and it has the desirable feature of eliminating the inactive variables in the course of the iterations - which can be used to speed up the algorithm. The numerical simulations conducted using both synthetic and real world (brain-network) data show that the proposed algorithm converges faster, in terms of the average number of iterations, than several existing methods in the literature.

Atherosclerosis is a lipid-driven inflammatory disease of the arteries. It causes increased thickness of the intima layer of an artery, due to build-up of, among others, lipid deposits. Eventually, this could cause rupture of the blood vessel’s wall, leading to thrombus forming and possibly to clinical events. Clinically, the degree of stenosis (degree of narrowing of the lumen) is used as a metric for risk assessment. However, narrow arteries do not necessarily rupture, while wide arteries can still rupture. Since rupture is a mechanical event, (mechanical) stress in the arteries could be a better metric to assess rupture. Stress could be calculated through a chosen constitutive model. However, these constitutive models require patient-specific constitutive parameters, which presently are challenging to obtain. To retrieve the patient-specific constitutive parameter(s), the Virtual Fields Method (VFM) could be used. This is a method based on the principle of virtual work. By minimizing the difference between the internal and external virtual work, the constitutive parameter(s) can be calculated. In this study, five atherosclerotic plaques were modeled with a neo-Hookean material model, subjected to intraluminal pressure in a Finite Element (FE) environment. The nodal deformations were used to apply the VFM in MATLAB. This was done at three levels: level 1 assumed that, for each model, a single constitutive parameter dictated the stress behavior of the entire atherosclerotic model. At level 2, the models were divided into four mechanically relevant components: intima, wall, calcified tissue, and lipid component. The ground truth for the constitutive parameter of each component was unique (120 kPa, 250 kPa, 5 kPa, and 423 kPa for intima, wall, lipid, and calcified tissue component respectively). For each of the components, a constitutive parameter was calculated. At the last level, the nodes, Mechanical Engineering | BioMechanical Design

We present a proximal augmented Lagrangian based solver for general quadratic programs (QPs), relying on semismooth Newton iterations with exact line search to solve the inner subproblems. The exact line search reduces in this case to finding the zero of a one-dimensional monotone, piecewise affine function and can be carried out very efficiently. Our algorithm requires the solution of a linear system at every iteration, but as the matrix to be factorized depends on the active constraints, efficient sparse factorization updates can be employed like in active-set methods. Both primal and dual residuals can be enforced down to strict tolerances and otherwise infeasibility can be detected from intermediate iterates. A C implementation of the proposed algorithm is tested and benchmarked against other state-of-the-art QP solvers for a large variety of problem data and shown to compare favorably against these solvers.

A PI design method for faulty networks is provided based on Linear Matrix Inequalities (LMIs). Feedback controllers for irrigation canals are designed based on LMIs, but sparsity constraints are also imposed to make zero the feedback control law elements not corresponding to the tuning PI parameters. Therefore, the design method is halfway between a PI controller and an optimal feedback control law, also providing stability guarantees up to a maximum probability of packet losses. The objective of the downstream controller is to maintain the water levels upstream from each downstream check structure of each canal pool, while gravity-offtake gates satisfy downstream water demands. The proposed approach is tested using the irrigation system of ASCE Test Canal 1 and compared with other tuning methods via simulation. Our results show that the design method can be a useful tool when dealing with control systems under faulty networks.

Operation research gives useful medical methods/gear employed in planning and management of agribusiness. Right from 1954 researchers commenced analyzing the applications of Operations studies in agriculture. Agriculture makes provision for food and is vital for the GDP of India with the ever-growing population. This paper makes a try to use numerous Operations research techniques inclusive of Linear Programming troubles to provide an explanation for how the combination of these strategies in agriculture will no longer only assist in reducing the triumphing problems in agriculture however additionally lead to better efficiency.

Over-the-air computation (AirComp) has emerged as a promising technique in future intelligent wireless networks, which enables swift functional computation among distributed wireless devices (WDs) by exploiting the superposition property of wireless channels. This paper studies a new hierarchical AirComp network over a large area, in which a set of intermediate relays are exploited to facilitate the massive data aggregation from a large number of WDs. Under this setup, we present a two-phase amplify-and-forward (AF) relaying protocol. In the first phase, the WDs simultaneously send their data to the relays, while in the second phase, the relays amplify the respectively received signals and concurrently forward them to the fusion center (FC) for aggregation. Our objective is to minimize the computational mean squared error (MSE) at the FC, by jointly optimizing the transmit coefficients of the WDs, the AF coefficients of the relays, and the de-noising factor of the FC, subject to their individual transmit power constraints. First, we consider the centralized design with global channel state information (CSI), in which the inter-relay signals can be exploited beneficially for data aggregation. In this case, we develop an alternating-optimization-based algorithm to obtain a high-quality solution to the computational MSE minimization problem. The obtained solution shows that the phase of the transmit coefficient at each WD is opposite to that of the WD-relay-FC channel to ensure the signal phase alignment at the FC, and the transmit power of each WD/relay follows a regularized composite-channel-inversion structure to strike a balance between minimizing the signal-magnitude-misalignment-induced error and the noise-induced error. Next, to reduce the signaling overhead caused by the centralized design, we consider an alternative decentralized design with partial CSI, in which the relays and the FC make their own decisions by only requiring the channel powe

In this article, we study an energy-efficient nonorthogonal multiple access (NOMA) enabled multiaccess edge computing (MEC) system with strict latency requirements. We aim to minimize the energy consumption of all users by optimizing the resource allocation (including power and computation resources) and subchannel assignment, subject to the given latency constraint. The formulated problem, however, is a nonconvex combinatorial optimization problem. Nevertheless, we decompose the problem into a resource allocation subproblem and a subchannel assignment subproblem, and then solve the two subproblems iteratively. On one hand, we investigate the hidden convexity of the resource allocation subproblem under the optimal conditions, and propose an efficient algorithm to optimally allocate the resources by dual decomposition methods. On the other hand, we formulate the subchannel assignment subproblem into an integer linear programming problem and strictly prove that the problem is nondeterministic polynomial-time hard. We then solve it optimally by branch-and-bound methods, which is shown to be efficient in extensive simulations. Moreover, through considerable simulation results, we show that our proposed algorithm helps greatly reduce users’ energy consumption when communication resources (e.g., bandwidth) are limited. Additionally, it is verified that NOMA outperforms orthogonal multiple access in multiuser latency-sensitive MEC systems.

In this letter, we propose HV-Net, a new method for hypervolume approximation in evolutionary multi-objective optimization. The basic idea of HV-Net is to use DeepSets, a deep neural network with permutation invariant property, to approximate the hypervolume of a non-dominated solution set. The input of HV-Net is a non-dominated solution set in the objective space, and the output is an approximated hypervolume value of this solution set. The performance of HV-Net is evaluated through computational experiments by comparing it with two commonly-used hypervolume approximation methods (i.e., point-based method and line-based method). Our experimental results show that HV-Net outperforms the other two methods in terms of both the approximation error and the runtime, which shows the potential of using deep learning techniques for hypervolume approximation. IEEE

We proposed network-decentralized control strategies, in which each actuator can exclusively rely on local information, without knowing the network topology and the external input, ensuring that the flow asymptotically converges to the optimal one with respect to the p -norm. For 1 < p < ∞ , the flow converges to a unique constant optimal up∗. We show that the state converges to the optimal Lagrange multiplier of the optimization problem. Then, we consider networks where the flows are affected by unknown spontaneous dynamics and the buffers need to be driven exactly to a desired set-point. We propose a network-decentralized proportional-integral controller that achieves this goal along with asymptotic flow optimality; now it is the integral variable that converges to the optimal Lagrange multiplier. The extreme cases p=1 and p=∞ are of some interest since the former encourages sparsity of the solution while the latter promotes fairness. Unfortunately, for p=1 or p=∞ these strategies become discontinuous and lead to chattering of the flow, hence no optimality is achieved. We then show how to approximately achieve the goal as the limit for p 1 or p ∞., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Team Tamas Keviczky

The development of Multi-access edge computing (MEC) has resulted from the requirement for supporting next generation mobile services, which need high capacity, high reliability and low latency. The key issue in such MEC architectures is to decide which edge nodes will be employed for serving the needs of the different end users. Here, we take a fresh look into this problem by focusing on the minimization of migration events rather than focusing on maximizing usage of resources. This is important because service migrations can create significant service downtime to applications that need low latency and high reliability, in addition to increasing traffic congestion in the underlying network. This paper introduces a priority induced service migration minimization (PrISMM) algorithm, which aims at minimizing service migration for both high and low priority services, through the use of Markov decision process, learning automata and combinatorial optimization. We carry out extensive simulations and produce results showing its effectiveness in reducing the mean service downtime of lower priority services and the mean admission time of the higher priority services., Embedded and Networked Systems

This paper studies the reduction (abstraction) of finite-state transition systems for control synthesis problems. We revisit the notion of alternating simulation equivalence (ASE), a more relaxed condition than alternating bisimulations, to relate systems and their abstractions. As with alternating bisimulations, ASE preserves the property that the existence of a controller for the abstraction is necessary and sufficient for a controller to exist for the original system. Moreover, being a less stringent condition, ASE can reduce systems further to produce smaller abstractions. We provide an algorithm that produces minimal AS equivalent abstractions. The theoretical results are then applied to obtain (un)schedulability certificates of periodic event-triggered control systems sharing a communication channel. A numerical example illustrates the results., Team Tamas Keviczky, Team Manuel Mazo Jr

We consider noisy communications and storage systems that are hampered by varying offset of unknown magnitude such as low-frequency signals of unknown amplitude added to the sent signal. We study and analyze a new detection method whose error performance is independent of both unknown base offset and offset's slew rate. The new method requires, for a codeword length n ≥ 12, less than 1.5 dB more noise margin than Euclidean distance detection. The relationship with constrained codes based on mass-centered codewords and the new detection method is discussed., Discrete Mathematics and Optimization

This paper develops an efficient solution for super-resolution two-dimensional (2D) harmonic retrieval from multiple measurement vectors (MMV). Given the sample covariance matrix constructed from the MMV, a gridless compressed sensing approach is proposed based on the atomic norm minimization (ANM). In the approach, our key step is to perform a redundancy reduction (RR) transformation that effectively reduces the large problem size at hand, without loss of useful frequency information. For uncorrelated sources, the transformed 2D covariance matrices in the RR domain retain a salient structure, which permits a sparse representation over a matrix-form atom set with decoupled 1D frequency components. Accordingly, the decoupled ANM (D-ANM) framework can be applied for super-resolution 2D frequency estimation. Moreover, the resulting RR-enabled D-ANM technique, termed RR-D-ANM, further allows an efficient relaxation under certain conditions, which leads to low computational complexity of the same order as the 1D case. Simulation results verify the advantages of our solutions over benchmark methods, in terms of higher computational efficiency and detectability for 2D harmonic retrieval., Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Signal Processing Systems

We proposed network-decentralized control strategies, in which each actuator can exclusively rely on local information, without knowing the network topology and the external input, ensuring that the flow asymptotically converges to the optimal one with respect to the p -norm. For 1 < p < ∞ , the flow converges to a unique constant optimal up∗. We show that the state converges to the optimal Lagrange multiplier of the optimization problem. Then, we consider networks where the flows are affected by unknown spontaneous dynamics and the buffers need to be driven exactly to a desired set-point. We propose a network-decentralized proportional-integral controller that achieves this goal along with asymptotic flow optimality; now it is the integral variable that converges to the optimal Lagrange multiplier. The extreme cases p=1 and p=∞ are of some interest since the former encourages sparsity of the solution while the latter promotes fairness. Unfortunately, for p=1 or p=∞ these strategies become discontinuous and lead to chattering of the flow, hence no optimality is achieved. We then show how to approximately achieve the goal as the limit for p 1 or p ∞., Accepted Author Manuscript, Team Tamas Keviczky

The development of Multi-access edge computing (MEC) has resulted from the requirement for supporting next generation mobile services, which need high capacity, high reliability and low latency. The key issue in such MEC architectures is to decide which edge nodes will be employed for serving the needs of the different end users. Here, we take a fresh look into this problem by focusing on the minimization of migration events rather than focusing on maximizing usage of resources. This is important because service migrations can create significant service downtime to applications that need low latency and high reliability, in addition to increasing traffic congestion in the underlying network. This paper introduces a priority induced service migration minimization (PrISMM) algorithm, which aims at minimizing service migration for both high and low priority services, through the use of Markov decision process, learning automata and combinatorial optimization. We carry out extensive simulations and produce results showing its effectiveness in reducing the mean service downtime of lower priority services and the mean admission time of the higher priority services., Embedded and Networked Systems

This paper studies the reduction (abstraction) of finite-state transition systems for control synthesis problems. We revisit the notion of alternating simulation equivalence (ASE), a more relaxed condition than alternating bisimulations, to relate systems and their abstractions. As with alternating bisimulations, ASE preserves the property that the existence of a controller for the abstraction is necessary and sufficient for a controller to exist for the original system. Moreover, being a less stringent condition, ASE can reduce systems further to produce smaller abstractions. We provide an algorithm that produces minimal AS equivalent abstractions. The theoretical results are then applied to obtain (un)schedulability certificates of periodic event-triggered control systems sharing a communication channel. A numerical example illustrates the results., Team Tamas Keviczky, Team Manuel Mazo Jr.

Operation research gives useful medical methods/gear employed in planning and management of agribusiness. Right from 1954 researchers commenced analyzing the applications of Operations studies in agriculture. Agriculture makes provision for food and is vital for the GDP of India with the ever-growing population. This paper makes a try to use numerous Operations research techniques inclusive of Linear Programming troubles to provide an explanation for how the combination of these strategies in agriculture will no longer only assist in reducing the triumphing problems in agriculture however additionally lead to better efficiency.

Atherosclerosis is a lipid-driven inflammatory disease of the arteries. It causes increased thickness of the intima layer of an artery, due to build-up of, among others, lipid deposits. Eventually, this could cause rupture of the blood vessel’s wall, leading to thrombus forming and possibly to clinical events. Clinically, the degree of stenosis (degree of narrowing of the lumen) is used as a metric for risk assessment. However, narrow arteries do not necessarily rupture, while wide arteries can still rupture. Since rupture is a mechanical event, (mechanical) stress in the arteries could be a better metric to assess rupture. Stress could be calculated through a chosen constitutive model. However, these constitutive models require patient-specific constitutive parameters, which presently are challenging to obtain. To retrieve the patient-specific constitutive parameter(s), the Virtual Fields Method (VFM) could be used. This is a method based on the principle of virtual work. By minimizing the difference between the internal and external virtual work, the constitutive parameter(s) can be calculated. In this study, five atherosclerotic plaques were modeled with a neo-Hookean material model, subjected to intraluminal pressure in a Finite Element (FE) environment. The nodal deformations were used to apply the VFM in MATLAB. This was done at three levels: level 1 assumed that, for each model, a single constitutive parameter dictated the stress behavior of the entire atherosclerotic model. At level 2, the models were divided into four mechanically relevant components: intima, wall, calcified tissue, and lipid component. The ground truth for the constitutive parameter of each component was unique (120 kPa, 250 kPa, 5 kPa, and 423 kPa for intima, wall, lipid, and calcified tissue component respectively). For each of the components, a constitutive parameter was calculated. At the last level, the nodes, Mechanical Engineering | BioMechanical Design

We consider noisy communications and storage systems that are hampered by varying offset of unknown magnitude such as low-frequency signals of unknown amplitude added to the sent signal. We study and analyze a new detection method whose error performance is independent of both unknown base offset and offset's slew rate. The new method requires, for a codeword length n ≥ 12, less than 1.5 dB more noise margin than Euclidean distance detection. The relationship with constrained codes based on mass-centered codewords and the new detection method is discussed., Optimization

Tesi en modalitat compendi de publicacions, This doctoral thesis addresses topology optimization problems at a single scale. Based on this purpose, a new topology optimization approach is developed in order to improve existing and widespread techniques in the research community on the topic. The proposed technique presents several characteristics that overcome some of the well-known difficulties in topological optimization while maintaining a considerable degree of simplicity. In the first place, the formulation of the topological optimization technique is presented, as well as its algorithm. The method is based on 4 fundamental features: (1) the use of a 1-0 characteristic function, as well as the precise identification of the material boundaries from a discrimination function (0-level-set function), (2) the definition of a topological derivative consistent with the ersatz method (used in the state problem), as an approximation to the exact topological derivative, (3) the inclusion of a Laplacian regularization with minimum size control of the different components, and (4) the formulation of an analytical optimality condition aiming at the optimal topology solution. The approach is applied to different topology optimization problems, well-reported in the literature and used as numerical benchmarks (in structural and thermal problems), to examine their performance. In these fields, stiffness and conductivity maximization problems are considered for validation, respectively. In addition, different topological optimization problems of major engineering interest are tackled, including the design of compliant mechanisms within the structural field and thermal cloaking devices within the thermal field. Finally, a comparison of the formulation with other existing topology optimization techniques is performed, including (1) SIMP, (2) ESO/BESO, and (3) Level-set with Hamilton-Jacobi as the updating equation. The analysis of the results provides a comparison in terms of the quality of the topology of each method, the, El objetivo de esta tesis doctoral es abordar el problema de optimización topológica a una única escala. En base a este propósito, se desarrolla una nueva técnica de optimización capaz de competir con técnicas ya existentes y extendidas entre la comunidad investigadora sobre el tema. Esta técnica presenta características que superan algunas de las dificultades bien conocidas en optimización topológica manteniendo un buen grado de simplicidad. En primer lugar, se presenta la formulación de la técnica de optimización topológica, así como su algoritmia. El método se fundamenta en 4 aspectos básicos: (1) la utilización de una función característica 1-0, así como la definición precisa de las fronteras materiales a partir de una función de discriminación (isonivel 0 de la función level-set), (2) la definición de una derivada topológica coherente con el método ersatz (utilizado en la ecuación de estado), como aproximación a la derivada topológica exacta, (3) la inclusión de una regularización Laplaciana con control de tamaño mínimo de los diferentes componentes, y (4) la definición de una condición de optimalidad analítica para la determinación de la solución óptima de la topología. La metodología se aplica a diferentes problemas de optimización topológica bien detallados en la literatura y utilizados como ensayos numéricos para examinar su respuesta frente a problemas estructurales y térmicos. En estos campos, se incluyen problemas de maximización de la rigidez y de la conductividad, respectivamente. Además, se resuelven diferentes problemas de optimización topológica con gran interés ingenieril en los campos estructurales con el diseño de mecanismos y térmicos con el diseño de dispositivos de camuflaje térmicos. Finalmente, se realiza una comparación de la formulación con otras técnicas ya existentes, por ejemplo: (1) SIMP, (2) ESO/BESO, y (3) Level-set con Hamilton-Jacobi como ecuación de evolución. El análisis de los resultados permite comparar la calidad de la topolog, Postprint (published version)

Operation research gives useful medical methods/gear employed in planning and management of agribusiness. Right from 1954 researchers commenced analyzing the applications of Operations studies in agriculture. Agriculture makes provision for food and is vital for the GDP of India with the ever-growing population. This paper makes a try to use numerous Operations research techniques inclusive of Linear Programming troubles to provide an explanation for how the combination of these strategies in agriculture will no longer only assist in reducing the triumphing problems in agriculture however additionally lead to better efficiency.

This paper develops an efficient solution for super-resolution two-dimensional (2D) harmonic retrieval from multiple measurement vectors (MMV). Given the sample covariance matrix constructed from the MMV, a gridless compressed sensing approach is proposed based on the atomic norm minimization (ANM). In the approach, our key step is to perform a redundancy reduction (RR) transformation that effectively reduces the large problem size at hand, without loss of useful frequency information. For uncorrelated sources, the transformed 2D covariance matrices in the RR domain retain a salient structure, which permits a sparse representation over a matrix-form atom set with decoupled 1D frequency components. Accordingly, the decoupled ANM (D-ANM) framework can be applied for super-resolution 2D frequency estimation. Moreover, the resulting RR-enabled D-ANM technique, termed RR-D-ANM, further allows an efficient relaxation under certain conditions, which leads to low computational complexity of the same order as the 1D case. Simulation results verify the advantages of our solutions over benchmark methods, in terms of higher computational efficiency and detectability for 2D harmonic retrieval., taverne, Circuits and Systems

Difference-of-convex (DC) optimization problems are shown to be equivalent to the minimization of a Lipschitz-differentiable “envelope”. A gradient method on this surrogate function yields a novel (sub)gradient-free proximal algorithm which is inherently parallelizable and can handle fully nonsmooth formulations. Newton-type methods such as L-BFGS are directly applicable with a classical linesearch. Our analysis reveals a deep kinship between the novel DC envelope and the forward-backward envelope, the former being a smooth and convexity-preserving nonlinear reparametrization of the latter.

In industries, the completion time of job problems in the manufacturing unit has risen significantly. In several types of current study, the job's completion time, or makespan, is reduced by taking straight paths, which is time-consuming. In this paper, we used an Improved Ant Colony Optimization and Tabu Search (ACOTS) algorithm to solve this problem by precisely defining the fault occurrence location in order to rollback. We have used a short-term memory-based rollback recovery strategy to minimise the job's completion time by rolling back to its own short-term memory. The recent movements in Tabu quest are visited using short term memory. As compared to the ACO algorithm, our proposed ACOTS-Cmax solution is more efficient and takes less time to complete.

In industries, the completion time of job problems in the manufacturing unit has risen significantly. In several types of current study, the job's completion time, or makespan, is reduced by taking straight paths, which is time-consuming. In this paper, we used an Improved Ant Colony Optimization and Tabu Search (ACOTS) algorithm to solve this problem by precisely defining the fault occurrence location in order to rollback. We have used a short-term memory-based rollback recovery strategy to minimise the job's completion time by rolling back to its own short-term memory. The recent movements in Tabu quest are visited using short term memory. As compared to the ACO algorithm, our proposed ACOTS-Cmax solution is more efficient and takes less time to complete.

Intelligent reflecting surfaces (IRSs) have emerged as a revolutionary solution to enhance wireless communications by changing propagation environment in a cost-effective and hardware-efficient fashion. In addition, symbol-level precoding (SLP) has attracted considerable attention recently due to its advantages in converting multiuser interference (MUI) into useful signal energy. Therefore, it is of interest to investigate the employment of IRS in symbol-level precoding systems to exploit MUI in a more effective way by manipulating the multiuser channels. In this article, we focus on joint symbol-level precoding and reflecting designs in IRS-enhanced multiuser multiple-input single-output (MU-MISO) systems. Both power minimization and quality-of-service (QoS) balancing problems are considered. In order to solve the joint optimization problems, we develop an efficient iterative algorithm to decompose them into separate symbol-level precoding and block-level reflecting design problems. An efficient gradient-projection-based algorithm is utilized to design the symbol-level precoding and a Riemannian conjugate gradient (RCG)-based algorithm is employed to solve the reflecting design problem. Simulation results demonstrate the significant performance improvement introduced by the IRS and illustrate the effectiveness of our proposed algorithms.

Role of optimization in engineering design is prominent one with the advent of computers. Optimization has become a part of computer aided design activities. It is prima rily being used in those design activities in which the goal is not only to achieve just a feasible design, but also a des ign objective. In most engineering design activities, the design objective could be simply to minimize the cost of production or to maximize the efficiency of the production. An optimization algorithm is a procedure which is executed it eratively by comparing various solutions till the optimum or a satisfactory solution is found. In many industri al design activities, optimization is achieved indirectly by comparing a few chosen design solutions and accept ing the best solution. This simplistic approach never guarantees and optimization algorithms being with one or more d esign solutions supplied by the user and then iteratively check new design the true optimum solution. There ar e two distinct types of optimization algorithms which are in use today.

In the following manuscript we will show as a starting point a theoretical analysis of the gradient method, known as one of the first descent methods, and from this we will identify the strength of the conjugate gradient methods. Taking an objective function,we will determine the values that optimize it by means of different methods, indicating the differences of geometric type that these have. Different systems will be used, in order to serve as a test, obtaining their solution in each case and finding the speed at which they converge in accordance with the conjugate gradient methods proposed by Hestenes-Stiefel and Fletcher-Reeves., En el siguiente manuscrito mostraremos como punto de inicio un análisis teórico del método de gradiente, conocido como unos de los primeros métodos de descenso, y a partir de ello identificar la fortaleza de los métodos del gradiente conjugado. Tomando una función objetivo determinaremos los valores que la optimizan mediante diferentes métodos indicando las diferencias de tipo geométricoque estos tengan. Se usarán distintos sistemas , con el fin de que sirvan de prueba obteniendo en cada caso su solución y encontrando la velocidad en que convergen de conformidad con los métodos de gradiente conjugado propuestos por Hestenes-Stiefel y Fletcher-Reeves.

Line spectral estimation is a classical signal processing problem that aims to estimate the line spectra from their signal which is contaminated by deterministic or random noise. Despite a large body of research on this subject, the theoretical understanding of this problem is still elusive. In this paper, we introduce and quantitatively characterize the two resolution limits for the line spectral estimation problem under deterministic noise: one is the minimum separation distance between the line spectra that is required for exact detection of their number, and the other is the minimum separation distance between the line spectra that is required for a stable recovery of their supports. The quantitative results imply a phase transition phenomenon in each of the two recovery problems, and also the subtle difference between the two. We further propose a sweeping singular-value-thresholding algorithm for the number detection problem and conduct numerical experiments. The numerical results confirm the phase transition phenomenon in the number detection problem. IEEE