Energy Minimization for Heterogenous Traffic Coexistence with Puncturing in Mobile Edge Computing-Based Industrial Internet of Things

Puncturing has been recognized as a promising technology to cope with the coexistence problem of enhanced mobile broadband(eMBB) and ultra-reliable low latency communications(URLLC)traffic. However, the steady performance of eMBB traffic while meeting the requirements of URLLC traffic with puncturing is a major challenge in some realistic scenarios. In this paper, we pay attention to the timely and energy-efficient processing for eMBB traffic in the industrial Internet of Things(IIoT), where mobile edge computing(MEC) is employed for data processing. Specifically, the performance of eMBB traffic and URLLC traffic in a MEC-based IIoT system is ensured by setting the threshold of tolerable delay and outage probability, respectively. Furthermore,considering the limited energy supply, an energy minimization problem of eMBB device is formulated under the above constraints, by jointly optimizing the resource blocks(RBs) punctured by URLLC traffic, data offloading and transmit power of eMBB device. With Markov's inequality, the problem is reformulated by transforming the probabilistic outage constraint into a deterministic constraint. Meanwhile, an iterative energy minimization algorithm(IEMA) is proposed.Simulation results demonstrate that our algorithm has a significant reduction in the energy consumption for eMBB device and achieves a better overall effect compared to several benchmarks.

Modeling Solid Waste Minimization Performance at Source in Dar es Salaam City, Tanzania

Municipal solid waste generation is strongly linked to rising human population and expanding urban areas, with significant implications on urban metabolism as well as space and place values redefinition. Effective management performance of municipal solid waste management underscores the interdisciplinarity strategies. Such knowledge and skills are paramount to uncover the sources of waste generation as well as means of waste storage, collection, recycling, transportation, handling/treatment, disposal, and monitoring. This study was conducted in Dar es Salaam city. Driven by the curiosity model of the solid waste minimization performance at source, study data was collected using focus group discussion techniques to ward-level local government officers, which was triangulated with literature and documentary review. The main themes of the FGD were situational factors (SFA) and local government by-laws (LGBY). In the FGD session, sub-themes of SFA tricked to understand how MSW minimization is related to the presence and effect of services such as land use planning, availability of landfills, solid waste transfer stations, material recovery facilities, incinerators, solid waste collection bins, solid waste trucks, solid waste management budget and solid waste collection agents. Similarly, FGD on LGBY was extended by sub-themes such as contents of the by-law, community awareness of the by-law, and by-law enforcement mechanisms. While data preparation applied an analytical hierarchy process, data analysis applied an ordinary least square (OLS) regression model for sub-criteria that explain SFA and LGBY;and OLS standard residues as variables into geographically weighted regression with a resolution of 241 × 241 meter in ArcMap v10.5. Results showed that situational factors and local government by-laws have a strong relationship with the rate of minimizing solid waste dumping in water bodies (local R square = 0.94).

Geophysical data sparse reconstruction based on L0-norm minimization

Missing data are a problem in geophysical surveys, and interpolation and reconstruction of missing data is part of the data processing and interpretation. Based on the sparseness of the geophysical data or the transform domain, we can improve the accuracy and stability of the reconstruction by transforming it to a sparse optimization problem. In this paper, we propose a mathematical model for the sparse reconstruction of data based on the LO-norm minimization. Furthermore, we discuss two types of the approximation algorithm for the LO- norm minimization according to the size and characteristics of the geophysical data： namely, the iteratively reweighted least-squares algorithm and the fast iterative hard thresholding algorithm. Theoretical and numerical analysis showed that applying the iteratively reweighted least-squares algorithm to the reconstruction of potential field data exploits its fast convergence rate, short calculation time, and high precision, whereas the fast iterative hard thresholding algorithm is more suitable for processing seismic data, moreover, its computational efficiency is better than that of the traditional iterative hard thresholding algorithm.

OPTIMAL PERFORMANCE OF THERMOELECTRIC REFRIGERATION SYSTEM USING ENTROPY GENERATION MINIMIZATION METHOD

With the entropy generation minimization （EGM） method, the thermodynamical performance optimization in a thermoelectric refrigeration system is studied. The optimization is affected by the irreversibility of heat transfer caused by finite temperature differences, the heat leak between external heat reservoirs and the internal dissipation of working fluids. EGM is taken as an objective function for the optimization. The objective function and design parameters are obtained. Optimal performance curves are presented by thermal and electronic parameters. Effects of these parameters on general and optimal performances are investigated. Results are helpful in determining optimal design conditions in real thermoelectric refrigeration systems.

A multiplicative Gauss-Newton minimization algorithm:Theory and application to exponential functions

Multiplicative calculus(MUC)measures the rate of change of function in terms of ratios,which makes the exponential functions significantly linear in the framework of MUC.Therefore,a generally non-linear optimization problem containing exponential functions becomes a linear problem in MUC.Taking this as motivation,this paper lays mathematical foundation of well-known classical Gauss-Newton minimization(CGNM)algorithm in the framework of MUC.This paper formulates the mathematical derivation of proposed method named as multiplicative Gauss-Newton minimization(MGNM)method along with its convergence properties.The proposed method is generalized for n number of variables,and all its theoretical concepts are authenticated by simulation results.Two case studies have been conducted incorporating multiplicatively-linear and non-linear exponential functions.From simulation results,it has been observed that proposed MGNM method converges for 12972 points,out of 19600 points considered while optimizing multiplicatively-linear exponential function,whereas CGNM and multiplicative Newton minimization methods converge for only 2111 and 9922 points,respectively.Furthermore,for a given set of initial value,the proposed MGNM converges only after 2 iterations as compared to 5 iterations taken by other methods.A similar pattern is observed for multiplicatively-non-linear exponential function.Therefore,it can be said that proposed method converges faster and for large range of initial values as compared to conventional methods.

Integral Global Minimization of Constrained Problems with Discontinuous Penalty Functions

A class of discontinuous penalty functions was proposed to solve constrained minimization problems with the integral approach to global optimization, m-mean value and v-variance optimality conditions of a constrained and penalized minimization problem were investigated. A nonsequential algorithm was proposed. Numerical examples were given to illustrate the effectiveness of the algorithm.

Thermodynamic analysis of combined reforming process using Gibbs energy minimization method: In view of solid carbon formation

Thermodynamic analysis was applied to study combined partial oxidation and carbon dioxide reforming of methane in view of carbon formation. The equilibrium calculations employing the Gibbs energy minimization were performed upon wide ranges of pressure （1-25 atm）, temperature （600-1300 K）, carbon dioxide to methane ratio （0-2） and oxygen to methane ratio （0-1）. The thermodynamic results were compared with the results obtained over a Ru supported catalyst. The results revealed that by increasing the reaction pressure methane conversion decreased. Also it was found that the atmospheric pressure is the preferable pressure for both dry reforming and partial oxidation of methane and increasing the temperature caused increases in both activity of carbon and conversion of methane. The results clearly showed that the addition of O2 to the feed mixture could lead to a reduction of carbon deposition.

Concave Minimization for Sparse Solutions of Absolute Value Equations

Based on concave function, the problem of finding the sparse solution of absolute value equations is relaxed to a concave programming, and its corresponding algorithm is proposed, whose main part is solving a series of linear programming. It is proved that a sparse solution can be found under the assumption that the connected matrixes have range space property(RSP). Numerical experiments are also conducted to verify the efficiency of the proposed algorithm.

Nonmonotone Adaptive Trust Region Algorithms with Indefinite Dogleg Path for Unconstrained Minimization

In this paper, we combine the nonmonotone and adaptive techniques with trust region method for unconstrained minimization problems. We set a new ratio of the actual descent and predicted descent. Then, instead of the monotone sequence, the nonmonotone sequence of function values are employed. With the adaptive technique, the radius of trust region △k can be adjusted automatically to improve the efficiency of trust region methods. By means of the Bunch-Parlett factorization, we construct a method with indefinite dogleg path for solving the trust region subproblem which can handle the indefinite approximate Hessian Bk. The convergence properties of the algorithm are established. Finally, detailed numerical results are reported to show that our algorithm is efficient.

Waste Minimization Through Process Integration and Multi-objective Optimization

By avoiding or reducing the production of waste, waste minimization is an effective approach to solve the pollution problem in chemical industry. Process integration supported by multi-objective optimization provides a framework for process design or process retrofit by simultaneously optimizing on the aspects of environment and economics. Multi-objective genetic algorithm is applied in this area as the solution approach for the multi-objective optimization problem.

Color Correction for Multi-view Video Using Energy Minimization of View Networks

Systems using numerous cameras are emerging in many fields due to their ease of production and reduced cost, and one of the fields where they are expected to be used more actively in the near future is in image-based rendering （IBR）. Color correction between views is necessary to use multi-view systems in IBR to make audiences feel comfortable when views are switched or when a free viewpoint video is displayed. Color correction usually involves two steps： the first is to adjust camera parameters such as gain, brightness, and aperture before capture, and the second is to modify captured videos through image processing. This paper deals with the latter, which does not need a color pattern board. The proposed method uses scale invariant feature transform （SIFT） to detect correspondences, treats RGB channels independently, calculates lookup tables with an energy-minimization approach, and corrects captured video with these tables. The experimental results reveal that this approach works well.

Impact of immunosuppression minimization and withdrawal in long-term hepatitis C virus liver transplant recipients

AIM: To investigate the effects of different immunosuppressive regimens and avoidance on fibrosis progression in hepatitis C virus (HCV) liver transplant (LT) recipients.

Delay Minimization for Intelligent Reflecting Surface Assisted Federated Learning

Federated learning(FL), which allows multiple mobile devices to cooperatively train a machine learning model without sharing their data with the central server, has received widespread attention.However, the process of FL involves frequent communications between the server and mobile devices,which incurs a long latency. Intelligent reflecting surface(IRS) provides a promising technology to address this issue, thanks to its capacity to reconfigure the wireless propagation environment. In this paper, we exploit the advantage of IRS to reduce the latency of FL. Specifically, we formulate a latency minimization problem for the IRS assisted FL system, by optimizing the communication resource allocations including the devices’ transmit-powers, the uploading time, the downloading time, the multi-user decomposition matrix and the phase shift matrix of IRS. To solve this non-convex problem, we propose an efficient algorithm which is based on the Block Coordinate Descent(BCD) and the penalty difference of convex(DC) algorithm to compute the solution. Numerical results are provided to validate the efficiency of our proposed algorithm and demonstrate the benefit of deploying IRS for reducing the latency of FL. In particular, the results show that our algorithm can outperform the baseline of Majorization-Minimization(MM) algorithm with the fixed transmit-power by up to 30%.

STAP method based on atomic norm minimization with array amplitude-phase error calibration

In this paper,a space-time adaptive processing(STAP)method is proposed for the airborne radar with the array amplitude-phase error considered,which is based on atomic norm minimization(ANM).In the conventional ANM-based STAP method,the influence of the array amplitude-phase error is not considered and restrained,which inevitably causes performance deterioration.To solve this problem,the array amplitude-phase error is firstly estimated.Then,by pre-estimating the array amplitude-phase error information,a modified ANM model is built,in which the array amplitude-phase error factor is separated from the clutter response and the clutter covariance matrix(CCM)to improve the estimation accuracy of the CCM.To prove that the atomic norm theory is applicable in the presence of the array amplitude-phase error,the clutter sparsity is analyzed in this paper.Meanwhile,simulation results demonstrate that the proposed method is superior to the state-of-the-art STAP method.Moreover,the measured data is used to verify the effectiveness of the proposed method.

MARVEL:Multi-Agent Reinforcement Learning for VANET Delay Minimization

In urban Vehicular Ad hoc Networks(VANETs),high mobility of vehicular environment and frequently changed network topology call for a low delay end-to-end routing algorithm.In this paper,we propose a Multi-Agent Reinforcement Learning(MARL)based decentralized routing scheme,where the inherent similarity between the routing problem in VANET and the MARL problem is exploited.The proposed routing scheme models the interaction between vehicles and the environment as a multi-agent problem in which each vehicle autonomously establishes the communication channel with a neighbor device regardless of the global information.Simulation performed in the 3GPP Manhattan mobility model demonstrates that our proposed decentralized routing algorithm achieves less than 45.8 ms average latency and high stability of 0.05%averaging failure rate with varying vehicle capacities.

3D Multiphase Piecewise Constant Level Set Method Based on Graph Cut Minimization

Segmentation of three-dimensional(3D) complicated structures is of great importance for many real applications.In this work we combine graph cut minimization method with a variant of the level set idea for 3D segmentation based on the Mumford-Shah model.Compared with the traditional approach for solving the Euler-Lagrange equation we do not need to solve any partial differential equations.Instead,the minimum cut on a special designed graph need to be computed.The method is tested on data with complicated structures.It is rather stable with respect to initial value and the algorithm is nearly parameter free.Experiments show that it can solve large problems much faster than traditional approaches.

Truncated sparse approximation property and truncated q-norm minimization

This paper considers approximately sparse signal and low-rank matrix’s recovery via truncated norm minimization minx∥xT∥q and minX∥XT∥Sq from noisy measurements.We first introduce truncated sparse approximation property,a more general robust null space property,and establish the stable recovery of signals and matrices under the truncated sparse approximation property.We also explore the relationship between the restricted isometry property and truncated sparse approximation property.And we also prove that if a measurement matrix A or linear map A satisfies truncated sparse approximation property of order k,then the first inequality in restricted isometry property of order k and of order 2k can hold for certain different constantsδk andδ2k,respectively.Last,we show that ifδs(k+|T^c|)<√(s-1)/s for some s≥4/3,then measurement matrix A and linear map A satisfy truncated sparse approximation property of order k.It should be pointed out that when Tc=Ф,our conclusion implies that sparse approximation property of order k is weaker than restricted isometry property of order sk.

Minimization of the Flowrate of Fresh Water and Corresponding Regenerated Water in Water-using System with Regeneration Reuse

A sequential three-step programming method is proposed for determining the minimum flowrate of fresh water and corresponding regenerated water in water-using system of single contaminant with regeneration reuse. In step 1, a programming with the objective of min fws is used to determine the minimum flowrate of fresh water, in which the mathematical representation is a mixed integer nonlinear programming (MINLP1). Then under the same constraints with step 1, a programming with the objective of min freg in step 2 and a programming with the objective of min Cr in step 3 are subsequently used to determine the minimum flowrate of regenerated water and the minimum inlet concentration to regeneration process corresponding to the minimum flowrate of fresh water based on step 1. The method is easy to apply because we only need to change the objective function but keep the constraints constant to go along the following steps after step 1. In addition, the relationship between the fresh water flowrate required, fws and inlet concentration to regeneration process, Cr, is investigated. It is found that there exist three relationships between fws and Cr, which indicate three possibilities for C\>: below the pinch, above the pinch or at the pinch. Therefore, a new conclusion is drawn, which differs from that 'regeneration of water at pinch minimizes fresh water flowrate' derived in literature and indicates that in some cases, regeneration at other point also minimizes fresh water flowrate.

Variance minimization for continuous-time Markov decision processes: two approaches

This paper studies the limit average variance criterion for continuous-time Markov decision processes in Polish spaces. Based on two approaches, this paper proves not only the existence of solutions to the variance minimization optimality equation and the existence of a variance minimal policy that is canonical, but also the existence of solutions to the two variance minimization optimality inequalities and the existence of a variance minimal policy which may not be canonical. An example is given to illustrate all of our conditions.

On Cost Minimization for Cache-Enabled D2D Networks with Recommendation

To accommodate the tremendous increase of mobile data traffic,cache-enabled device-to-device(D2D)communication has been taken as a promising technique to release the heavy burden of cellular networks since popular contents can be pre-fetched at user devices and shared among subscribers.As a result,cellular traffic can be offloaded and an enhanced system performance can be attainable.However,due to the limited cache capacity of mobile devices and the heterogeneous preferences among different users,the requested contents are most likely not be proactively cached,inducing lower cache hit ratio.Recommendation system,on the other hand,is able to reshape users’request schema,mitigating the heterogeneity to some extent,and hence it can boost the gain of edge caching.In this paper,the cost minimization problem for the social-aware cache-enabled D2D networks with recommendation consideration is investigated,taking into account the constraints on the cache capacity budget and the total number of recommended files per user,in which the contents are sharing between the users that trust each other.The minimization problem is an integer non-convex and non-linear programming,which is in general NP-hard.Therewith,we propose a timeefficient joint recommendation and caching decision scheme.Extensive simulation results show that the proposed scheme converges quickly and significantly reduces the average cost when compared with various benchmark strategies.