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The output power of an ocean wave energy (WE) system has an intermittent and stochastic characteristic. WE output power can be transferred to the grid without sudden fluctuations when combined with a hybrid energy storage system (HESS) consisting of a battery pack and an ultracapacitor (UC) module. The study presented in this paper identifies the lowest-cost HESS sizing for WE systems by using a genetic algorithm (GA) optimization method. In this study, the system cost was reduced with the HESS cost and sizing study for ocean WE converter systems, and the battery was used effectively for a longer cycle. GA optimization has been applied in the field of HESS in ocean WE systems and has brought innovation to the literature with its optimum cost and sizing study. An optimum design model is presented considering the maximum/minimum voltage and current limits and the energy storage units’ temperature and depth of discharge parameters. The series and parallel connection calculations and the required number of battery and UC cells are given in the sizing section. The GA optimization was performed in Matlab, and the energy storage rate for the 625-kW system and the power and energy results of the energy storage units were given as a result of the optimum cost analysis. It has been calculated that 15936 battery cells are normally required for a 625-kW ocean WE converter system. As a result of the proposed optimally sized, cost-effective HESS, it is presented that a hybrid energy system can be used at a lower cost with 5548 battery cells and 2125 UC cells for the same performance and functionality.

The incorporation of Internet of Things (IoT) technology with agriculture has transformed several farming practices, bringing unparalleled simplicity and efficiency. This article explores the robust integration of IoT and blockchain technology(BIoT) in agricultural operations, offering insight into the resulting BIoT system’s design. This study investigates the potential benefits of merging the IoT and blockchain technologies in agriculture. A system for tracking plant growth using sensors and blockchain-integrated IoT has been developed and analyzed. Through empirical investigation, the report highlights greater efficiency, transparency, and security as significant benefits of BIoT architecture. A cost analysis is also conducted, showing that the system is cost-effective. The paper also proposes a mathematical formula for the calculation of infrastructure cost. It also looks at the problems of integrating BIoT in agricultural contexts, such as technological complications and the need for stakeholder participation. The findings indicate that the IoT can potentially revolutionize agricultural practices drastically, but its practical implementation would need careful consideration of technological, economic, and societal considerations. Empirical analysis is conducted to analyze the latency, throughput, and transaction scalability. The paper concludes with research suggestions, emphasizing the significance of interdisciplinary collaboration and the consequences of BIoT deployment in agriculture.

Artificial Intelligence (AI)-supported healthcare has seen a substantial rise in development in recent years. The health economic impact is a crucial factor in the decision-making process regarding AI adoption. This study aimed to analyze the latest research progress and evidence on the cost-effectiveness and clinical efficiency of healthcare AI software from various perspectives, as well as to identify future opportunities and remaining challenges. A review of global literature was conducted using two key databases, PubMed and Embase, along with other related bibliographic resources. The literature search yielded 1,178 unique articles, of which 31 were included in our analysis. These studies covered a wide variety of clinical use cases and healthcare domains, including disease diagnosis (n=13, 41.9%), risk analysis (n=6, 19.4%), screening or patient triage (n=6, 19.4%), treatment delivery (n=5, 16.1%), and patient engagement or follow-up (n=1, 3.2%). Among the included studies, 24 (77.4%) examined the cost-effectiveness of AI compared to standard human-based practices from the perspectives of patients, healthcare systems, payors, or society. The remaining 7 studies, including 5 clinical trials, concluded that AI can enhance clinical efficiency by shortening labor time or patient journey in the clinic. The findings of this targeted literature review demonstrated that leveraging AI in human decision-making has the potential to improve multilevel health outcomes. However, there is a shortage of prospective health economic studies, particularly long-term evaluations, highlighting the disparity between the rapid progress of AI and its lagging utilization in real-world practices.

Blockchain technology offers solutions to numerous network problems by leveraging distributed record-keeping and collaborative decision-making features. However, deployment considerations such as blockchain infrastructure cost, performance requirements, and scalability are often overlooked. This paper provides an in-depth perspective on deploying blockchain-based solutions for telecommunications networks, estimating costs, comparing infrastructure options (on-premises, IaaS, BaaS), and choosing a suitable blockchain platform. To that end, we identify the performance limitations of the proposed solution under various deployment infrastructures by studying two prominent use cases: one proposing a distributed marketplace solution for 5G slice brokering and another one on the decentralization of federated learning (FL) through blockchain. For the slice brokering use case, our experiments showed that sub-second latency could be achieved for a maximum transaction throughput in the range of 10 to 200 transactions per second (TPS), whereas use cases requiring a higher throughput (300 to 400 TPS) would need more computational resources. Meanwhile, the FL use case provided insights into the achievable accuracy of distributed learning under various blockchain settings (public, consortium, and private), which led to the understanding that private and consortium blockchains can achieve acceptable accuracy in significantly lower training times compared to public blockchains.

Random forests (RFs) use a collection of decision trees (DTs) to perform the classification or regression. RFs are adopted in a wide variety of machine learning (ML) applications, and they are finding increasing use also in scenarios at the extreme edge of the Internet of Things (TinyML) where memory constraints are particularly tight. This article addresses the optimization of the computational and storage costs for running DTs on the microcontroller units (MCUs) typically deployed in TinyML scenarios. We introduce three alternative DT kernels optimized for memory- and compute-limited MCUs, providing insight into the key memory-latency tradeoffs on an open-source RISC-V platform. We identify key bottlenecks and demonstrate that SW optimizations enable up to significant memory footprint and latency decrease. Experimental results show that the optimized kernels achieve up to 4.5 $\mu \text{s}$ latency, $4.8\times $ speedup, and 45% storage reduction against the widely-adopted naive DT design. We carry out a detailed performance and energy cost analysis of various optimized DT variants: the best approach requires just 8 instructions and 0.155 pJ per decision. [ABSTRACT FROM AUTHOR]

The evolving disease spectrum poses significant challenges to the asthma management, thus worsening health quality and increased financial burden on patients. However, potential dependency pattern in comorbidity spectrum remains unclear. We built comorbidity networks based on Bayesian networks utilizing 19604 asthma-patient hospitalization data to investigate dependency patterns among asthma comorbidities. We analyze static properties and trajectory behaviors of gender- and age-stratified asthmatic comorbidity networks. Results suggest that chronic obstructive pulmonary disease, respiratory failure, hypertension, atherosclerosis, and gastritis and duodenitis are the hubs of the asthma comorbidity network. They have a strong dependency pattern, while most of the associations among other comorbidities are sparse and weak. The strength of association between comorbidities is higher in female asthmatics than in males. Although the comorbidity network in children with asthma is simple and stable, the onset of common comorbidities as they age will enhance the association between comorbidities and thus increase the risk of developing other comorbidities. Furthermore, the more attributes of comorbidities, the stronger association with each other, and the greater risk of causing high treatment costs. Our study will help to dissect the asthma co-morbidity network and provide a basis for improving asthma management and cost control. [ABSTRACT FROM AUTHOR]

Lesion volume segmentation in medical imaging is an effective tool for assessing lesion/tumor sizes and monitoring changes in growth. Since manually segmentation of lesion volume is not only time-consuming but also requires radiological experience, current practices rely on an imprecise surrogate called response evaluation criteria in solid tumors (RECIST). Although RECIST measurement is coarse compared with voxel-level annotation, it can reflect the lesion’s location, length, and width, resulting in a possibility of segmenting lesion volume directly via RECIST measurement. In this study, a novel weakly-supervised method called RECISTSup is proposed to automatically segment lesion volume via RECIST measurement. Based on RECIST measurement, a new RECIST measurement propagation algorithm is proposed to generate pseudo masks, which are then used to train the segmentation networks. Due to the spatial prior knowledge provided by RECIST measurement, two new losses are also designed to make full use of it. In addition, the automatically segmented lesion results are used to supervise the model training iteratively for further improving segmentation performance. A series of experiments are carried out on three datasets to evaluate the proposed method, including ablation experiments, comparison of various methods, annotation cost analyses, visualization of results. Experimental results show that the proposed RECISTSup achieves the state-of-the-art result compared with other weakly-supervised methods. The results also demonstrate that RECIST measurement can produce similar performance to voxel-level annotation while significantly saving the annotation cost. [ABSTRACT FROM AUTHOR]

This paper proposes a predictive techno-economic analysis in terms of voltage stability and cost using regression-based machine learning (ML) models and effectiveness of the analysis is validated. Predictive analysis of a power system is proposed to address the need for faster and accurate analyses that would aid in the operation and control of modern power system. Several methods of analyses including metaheuristic optimization algorithms, artificial intelligence techniques and machine learning algorithms are being developed and used. Predictive ML models for two modified IEEE 14-bus and IEEE-30 bus systems, integrated with renewable energy sources (solar and wind) and reactive power compensative device (STATCOM) are proposed and developed with features that include hour of the day, solar irradiation, wind velocity, dynamic grid price and system load. An hour-wise input database for the model development is generated from monthly average data and hour-wise daily curves with normally distributed standard deviations. The data feasibility tests and output database generation is performed using MATLAB. Linear and higher order polynomial regression models are developed for the 8760hr database using Python 3.0 in JupyterLab and a best-fit predictive ML model is identified by analysing the coefficients of determination. The voltage stability and cost predictive ML models were tested for a 24hr input profile. The results obtained and the comparison with the expected values are furnished. Prediction of the outputs for the test data validate the accuracy of the developed model.

Federated learning (FL) is a distributed learning paradigm that enables a large number of mobile devices to collaboratively learn a model under the coordination of a central server without sharing their raw data. Despite its practical efficiency and effectiveness, the iterative on-device learning process (e.g., local computations and global communications with the server) incurs a considerable cost in terms of learning time and energy consumption, which depends crucially on the number of selected clients and the number of local iterations in each training round. In this paper, we analyze how to design adaptive FL in mobile edge networks that optimally chooses these essential control variables to minimize the total cost while ensuring convergence. We establish the analytical relationship between the total cost and the control variables with the convergence upper bound. To efficiently solve the cost minimization problem, we develop a low-cost sampling-based algorithm to learn the convergence related unknown parameters. We derive important solution properties that effectively identify the design principles for different optimization metrics. Practically, we evaluate our theoretical results both in a simulated environment and on a hardware prototype. Experimental evidence verifies our derived properties and demonstrates that our proposed solution achieves near-optimal performance for different optimization metrics for various datasets and heterogeneous system and statistical settings. [ABSTRACT FROM AUTHOR]

The objective of the work is to make Rameswaram, a region in Tamil Nadu self-sufficient for its energy needs by making use of hybrid energy to the maximum and utilizing fossil-based energy to the minimum. To calculate the wind and solar energy for a particular region, the wind speed and solar irradiance of that region are to be forecasted. Then the peak load requirement for a year of that region is identified and the installation capacities of wind and solar farms are fixed. Storage devices like batteries, are not used in this work. Hence all the power drawn from installed array of solar panels are utilized first to meet the hourly peak demands. If the power drawn from solar panels are insufficient to meet the demand, then the wind turbines are operated. The number of wind turbines to be operated will differ from time to time based on the demand that could not be met with solar power alone. The grid supply is used only in case of any deficiency from solar and wind power to meet the peak demand requirements. A new algorithm is proposed which will help in estimating the number of wind turbines to be operated to meet the hourly peak demands. Later the costs of utilizing energy from grid and renewable energy are estimated separately and the results compared. The entire process is automated for ease of operation using the proposed algorithm. This model can be extended to any region where this type of configuration is proposed.

In this article, cabling constraints of different photovoltaic (PV) configurations is addressed in three steps: 1) a cable selection criterion is developed in accordance with metric system defined by American Wire Gauge (AWG); 2) a mathematical model is designed, which estimates the cable length for any array size; and 3) cost functions are devised owing to estimate the cables capital expenditure. The aforementioned steps are developed for Series (S), Parallel (P), Series-Parallel (SP), Total-cross-tied (TCT), Bridge-Link (BL) and Honey-Comb (HC). A comprehensive mathematical model is developed for each of the above mentioned architectures in the context of cabling cost versus energy payback time. This cost analysis provides a clear snapshot to the designers of PV plant about the capital investments of cabling system of specific architecture and its potential energy payback time. At the end, a design example for each configuration is presented against an array size of 10 × 2 PV panels.

This study demonstrates a dispatching scheme of wind-solar hybrid power system (WSHPS) for a one-hour dispatching period for an entire day utilizing battery and supercapacitor hybrid energy storage subsystem (HESS). A frequency management approach is deployed to extend the longevity of the batteries through extensively utilizing the high energy density property of batteries and the high power density property of supercapacitors in the HESS framework. A low-pass filter (LPF) is employed to decouple the power between a battery and a supercapacitor (SC). The cost optimization of the HESS is computed based on the time constant of the LPF through extensive simulations in MATLAB/SIMULINK platform. The curve fitting and Particle Swarm Optimization approaches are applied to seek the optimum value of the LPF time constant. Several control algorithms as a function of the battery state of charge are developed to achieve accurate estimation of the grid reference power for each one-hour dispatching period. This estimation helps to minimize the energy storage cost, in addition to ensuring that the HESS has sufficient capacity for next-day operation. The optimum value of depth of discharge for HESS considering both cycling and calendar expenses has also been investigated for the best competitive energy storage cost for hourly dispatching the power of the WSHPS. This research also presents an economic comparison to investigate the significance of using different types of energy storage for hourly dispatching the WSHPS. The simulation results show that the presented HESS is superior to battery or SC-only operation.

This paper proposes on-driving experience sharing algorithms at junctions in infrastructure-assisted vehicles-to-everything networks. For the purpose, a millimeter-wave (mmWave) technology is used because it provides multi-Gbps data rates which is helpful for handling users’ short stay times at junctions and spatial reuse due to high beam directionality which is helpful for interference-avoidance among densely deployed vehicles at junctions. To realize on-driving experience sharing, the proposed algorithms focus on joint resource allocation and scheduling for 3GPP-compliant multiple unicast vehicle-to-vehicle (V2V) communications where the vehicles are group leaders (GLs) in 3GPP Mode 4(d). The resource allocation stands for the roadside unit (RSU) allocation to scheduled V2V GL links where RSU is essentially required for overcoming blockage by establishing two-hop relaying. Because vehicles stay for short times at junctions, this paper designs two algorithms without or with delay considerations. Without delay considerations, the joint optimization of RSU allocation and scheduling was originally formulated as mixed 0-1 non-convex optimization. However our proposed algorithm reformulates the problem into mixed 0-1 convex optimization, which is computationally easier to solve. With delay considerations, our proposed algorithm dynamically controls video contents frame rates for time-average on-driving video sharing quality maximization subject to delay constraints, inspired by Lyapunov optimization. Extensive simulation results demonstrate that our algorithms can significantly outperform in a variety of scenarios. Furthermore, we conduct the cost analysis for the proposed algorithms in terms of capital expenditure (CAPEX) and operating expenditure (OPEX). [ABSTRACT FROM AUTHOR]

Wireless communication is facing an ever increasing thirst for high-quality data transmission. However, this imposes high demands on the radio access networks (RANs), where optical fiber has been widely used for both the backhaul and fronthaul. However, the explosive escalation of wireless tele-traffic tests the limits of the RAN's revenue vs. cost trade-off, which motivates low-cost designs. Hence, we present a cost-efficient but high-performance radio over fiber (RoF)-aided RAN concept. We commence by introducing this motivation, followed by a brief introduction to analog RoF (A-RoF) and digitized RoF (D-RoF), as well as to wireless multiple-input multiple-output (MIMO) techniques. Then we present the centralized RAN (C-RAN) concept relying on A-RoF-aid-ed MIMO signal processing, where the MIMO signal is carried by fiber and processed optically in a central unit. Subsequently, we focus our attention on a C-RAN small cell application followed by our performance vs. cost analysis of the A-RoF system compared to that of its baseband counterparts, demonstrating that the A-RoF design is capable of reducing the RAN's total cost while meeting the Third Generation Partnership Proj-ect's requirements. [ABSTRACT FROM AUTHOR]

As parallel computing becomes increasingly important in many real-world applications, a batch sequential experimental design (BSED), which adds a batch of computer experiments per iteration and runs these simulations in parallel, is gaining popularity in surrogate modeling. This article proposes sequential online dispatch in design of experiments (SODDE) for single- and multiple-response surrogate modeling when multiple processors work in parallel but not independently. The proposed method includes several unique features: 1) it works with any popular acquisition function to select a single new sample point at each iteration; 2) it minimizes the idle time of all processors; 3) it rapidly updates the surrogate model; and 4) it dynamically reconstructs the surrogate model when a simulation process aborts, minimizing the impact incurred by the abortion. The effectiveness of SODDE is evaluated in one mathematical example and one industrial problem. The latter problem considers blade design of horizontal axis wind turbines (HAWTs). The cost of finding blade geometry that results in the desired aerodynamic behavior of HAWTs is estimated for BSED and SODDE. Relative to BSED, SODDE reduces the costs by up to approximately 31%. Note to Practitioners—Computer simulation models are often used to represent the behavior of complex engineered systems, such as automobiles, aircraft, and wind turbines. Although running computer simulations allows analyzing the performance of these systems, the simulations can still be computationally costly (e.g., hours of runtime for numerically solving a large number of differential equations in one simulation), which makes the performance analysis task challenging. It is important to design an innovative simulation process that replaces the original expensive simulation models by cheap-to-build surrogate models, and at the same time, minimize the computational costs for building the surrogate models. The new method developed in this article allows experimental designers to run batch sequential iterations and run these iterations in parallel. This, in engineering practice, will help meet the need for long and complex experimental designs scenarios in a parallel computing environment, where the computer “up time” (or runtime) needs to be maximized to save time and ultimately, money. Cost analysis results from an industrial problem involving the inverse design of wind turbine blades suggest that the proposed method reduces the computational costs by up to approximately 31% over an existing method. [ABSTRACT FROM AUTHOR]

In recent times, we have witnessed an increasing concern by governments and intelligence agencies to deploy mass-surveillance systems that help them fight terrorism. Although a government may be perfectly legitimate to do so, it is questionable whether a preventive-surveillance state is rational and cost-effective. In this paper, we conduct a theoretical analysis of the cost of such surveillance systems. Our analysis starts with a fairly well-known result in statistics, namely, the false-positive paradox. We propose a quantitative measure of the total cost of a monitoring program, and study a detection system that is designed to minimize it, subject to a constraint in the percentage of terrorists the agency wishes to capture. Our formulation is first illustrated by means of several simple albeit insightful examples of terrorist and innocent profiles. Then, we conduct an extensive experimental study from real-world socio-demographic data of jihadist terrorism in the U.K. and Spain, and provide insight into the rationality and cost-effectiveness of two countries with two of the biggest defense budgets in the world.

This paper reports an energy-efficient permanent magnet (PM) zero-holding-energy magnetic contactor (ZMC) that consumes no energy while maintaining an open or closed state. Conventional solenoid magnetic contactor (MC) technology has several critical drawbacks, including high continuous energy consumption, continuous vibration, and low reliability. The proposed ZMC substantially reduces energy consumption and heat generation, produces no vibration, and provides increased reliability. Traditional MC analysis typically requires computationally expensive finite element analysis (FEA). To reduce the analysis costs, a recently proposed fast and accurate nonlinear transient path energy method (NT-PEM) is utilized to significantly reduce analysis time and costs. Analysis accuracy was verified experimentally using a fabricated ZMC prototype. The economic and environmental benefits of the proposed ZMC confirm its significant potential as an energy-efficient eco-friendly alternative to traditional MC technology. ZMC is particularly promising for vehicular micro-grids due to the prolongation of the lifetime of batteries owing to reduced energy consumption. [ABSTRACT FROM AUTHOR]

The big video data size today imposes huge pressure on storage. The variation and aging induced memory failures significantly influence the video output quality. Recently, researchers have developed different memory designs for videos, deep learning, and other data-intensive applications, which enables better energy-quality tradeoffs with design constraints. Unfortunately, designing memory has been proven to be a very challenging problem due to: 1) various design constraints; 2) multiple memory bitcell design options; and 3) challenging layout integration and cost analysis using different memory technologies. In this paper, we develop novel mathematical models for optimizing embedded video memory design without applying a time-consuming and laborious ASIC design process. The problems are formulated as nonlinear programs and integer linear programs. Different SRAM designs and hybrid SRAM and DRAM designs are considered in our models. The results of the numerical studies show that by applying our proposed method the average mean square error of the video storage can be greatly reduced, even by more than 90% in many cases. [ABSTRACT FROM AUTHOR]

SQL injection (SQLi) is one of the chief threats to the security of database-driven Web applications. It can cause serious security issues such as authentication bypassing, privacy leakage, and arbitrary code execution. Dynamic testing techniques are used in SQLi vulnerability discovery, which de-facto approach is to maintain a collection of elaborately designed user inputs (aka. attack payloads) and based on it to compose malicious SQL queries to Web applications. Such techniques are effective to reveal SQLi threats before an application is released, thus reducing the cost of manual analysis, monitoring or postdeployment of other defensive mechanisms. However, because of the diversity of SQLi attacks and the difficulty of SQLi discovery, the process to execute payloads can be costly, time-consuming, and even risky. In this paper, we approach from a test case prioritization perspective to give a more effective SQLi discovery proposal, which is based on adaptive random testing with the aim to successfully trigger an SQLi within limited attempts. To evaluate our method, we conduct an experiment using three extensively adopted open source vulnerable benchmarks. The experiment results indicate that our method ART4SQLi can effectively improve the conventional random testing approach on three common benchmarks by more than 26% in reducing the number of SQLi attempts before accomplishing a successful injection. [ABSTRACT FROM AUTHOR]

In the era of Big Data, as vast amounts of data are collected and shared among collaborators or uploaded to the Internet, the attacks on data privacy become more and more serious. Compressive privacy (CP) is a kind of privacy-preserving dimension-reduced projection schemes such that the projected data can be well used for the intended utility task but not for malicious applications. Nevertheless, most of existing CP approaches belong to centralized processing, which are not applicable to the cases that data is dispersedly collected/stored at distributed nodes and cannot be centralized to one node for processing due to various reasons. To tackle this problem, we propose a distributed differential utility/cost analysis (dDUCA), in which each node in the network is only allowed to exchange and combine the compressive-and-lossy projection matrix with its one-hop neighbors. Using the projection matrix, the classification of the projected data is performed. Experiments on several datasets confirm the effectiveness of the proposed method in terms of both privacy protection and utility retention. [ABSTRACT FROM AUTHOR]

Featuring higher switching speed and lower losses, the silicon carbide mosfets (SiC mosfets) are widely used in higher power density and higher efficiency power electronic applications as a new solution. However, the increase of the switching speed induces oscillations, overshoots, electromagnetic interference (EMI), and even additional losses. In this paper, a novel active gate driver (AGD) for high-power SiC mosfets is presented to fully utilize its potential of high-speed characteristic under different operation temperatures and load currents. The principle of the AGD is based on drive voltage decrement during the voltage and current slopes since high dV/dt and dI/dt are the source of the overshoots, oscillations, and EMI problems. In addition, the optimal drive voltage switching delay time has been analyzed and calculated considering a tradeoff between switching losses and switching stresses. Compared to conventional gate driver with fixed drive voltage, the proposed AGD has the capability of suppressing the overshoots, oscillations, and reducing losses without compromising the EMI. Finally, the switching performance of the AGD was experimentally verified on 1.2 kV/300 A and 1.7 kV/300 A SiC mosfets in double pulse test under different operation temperatures and load currents. In addition, an EMI discussion and cost analysis were realized for AGD. [ABSTRACT FROM AUTHOR]

Next-generation heterogeneous cellular network (HCN) expects more and more deployment of the base stations and spectrum to achieve data rate in gigabits per second with seamless connectivity. Recently, small cell deployment has proven its effectiveness for increasing the coverage and area spectral efficiency (ASE) of the cellular network. Although, dense small cell deployment also increases the deployment cost and cross-tier interference of the network. This paper suggests switching-based mixed millimeter wave (mmW)/ultra high frequency (UHF) cellular network as a possible solution to overcome these challenges. In mixed mmW/UHF cellular network (named as MMUCN), mmW is used as the main transmission link, and UHF band is utilized as a backup link. In this paper, a theoretical framework is developed using tools from the stochastic geometry to analyze the outage performance, ASE, and deployment cost efficiency (DCE) of the MMUCN, which comprises existing macro base stations and mmW base stations. Optimal mmW base station densities constraints by the outage are calculated, which maximizes the ASE and DCE. Furthermore, a multiobjective optimization problem is formulated to maximize ASE and DCE simultaneously. Numerical results show that MMUCN outperforms UHF-based two-tier cellular network and efficiently mitigates the cross-tier interference of the system. [ABSTRACT FROM AUTHOR]

This paper introduces significant performance enhancements to the ac filterless LCC HVDC by including fixed series capacitors at the primary side of a converter transformer. In terms of technical performance, 1) the amount of active power that can be transmitted is increased by more than 60% compared with ac filterless LCC HVDC, especially under severe unbalanced fault such as single-phase fault (most common fault in power systems); 2) the required voltage level of the controllable capacitor for Commutation Failure (CF) elimination is reduced by more than 70%, which leads to considerable reductions of the associated costs and losses. In terms of economic performance, due to the reduction of the required voltage from controllable capacitors (hence, the number of power electronic devices), the cost of the proposed converter station is lower than that of the ac filterless LCC HVDC. Theoretical analysis is presented to illustrate the performance enhancements and select the size of the series capacitor. Simulation results for various kinds of faults and cost analysis are presented to validate the technical and economic performances of the proposed method. Comparisons are made with ac filterless LCC HVDC. Finally, various practical issues and possible solutions are discussed. [ABSTRACT FROM AUTHOR]

This paper introduces an analytical framework for assessing the cost-effectiveness of intervention strategies to reduce total joint replacement (TJR) readmissions. In such a framework, a machine learning-based readmission risk prediction model is developed to predict an individual TJR patient's risk of hospital readmission within 90 days post-discharge. Specifically, through data sampling and boosting techniques, we overcome the class imbalance problem by iteratively building an ensemble of models. Then, utilizing the results of the predictive model, and by taking into account the imbalanced misclassification costs between readmitted and nonreadmitted patients, a cost analysis framework is introduced to support decision making in selecting cost-effective intervention policies. Finally, using this framework, a case study at a community hospital is presented to demonstrate the applicability of the analysis. [ABSTRACT FROM AUTHOR]

We present a two-fold fusion framework that constructs comprehensive cost volumes for stereo matching. To this end, we develop fusion schemes at two key steps, i.e., a) the raw cost computation and b) the cost aggregation. Specifically, we commence by fusing both structure- and data-oriented features as raw costs. We then incorporate the guided filtered costs into the cross-based cost aggregation and obtain the fused aggregated costs. The fusion schemes at the two steps effectively complement each other and result in an accurate disparity map. Experiments on the Middlebury benchmark v3 demonstrate the state-of-the-art performance of our framework in terms of various metrics. [ABSTRACT FROM AUTHOR]

For vehicular networks, dropboxes are very useful for assisting the data dissemination, as they can greatly increase the contact probabilities between vehicles and reduce the data delivery delay. However, due to the costly deployment of dropboxes, it is impractical to deploy dropboxes in a dense manner. In this paper, we investigate how to deploy the dropboxes optimally by considering the tradeoff between the delivery delay and the cost of dropbox deployment. This is a very challenging issue due to the difficulty of accurate delay estimation and the complexity of solving the optimization problem. To address this issue, we first provide a theoretical framework to estimate the delivery delay accurately. Then, based on the idea of dimension enlargement and dynamic programming, we design a novel optimal dropbox deployment algorithm (ODDA) to obtain the optimal deployment strategy. We prove that ODDA has a fast convergence speed, which is less than $\kappa$ ( $\kappa < n$ ) iterations for convergence. We also prove that the computational complexity of ODDA is $O(n \kappa m \;\log m)$ , i.e., ODDA has a polynomial computational complexity for a given $m$ , the number of dropboxes for deployment. Performance evaluation by simulation demonstrates the superior performance of the proposed strategies compared with the benchmark methods. [ABSTRACT FROM AUTHOR]

The excitation and vibration triggered by the long-term operation of railway vehicles inevitably result in defective states of catenary support devices. With the massive construction of high-speed electrified railways, automatic defect detection of diverse and plentiful fasteners on the catenary support device is of great significance for operation safety and cost reduction. Nowadays, the catenary support devices are periodically captured by the cameras mounted on the inspection vehicles during the night, but the inspection still mostly relies on human visual interpretation. To reduce the human involvement, this paper proposes a novel vision-based method that applies the deep convolutional neural networks (DCNNs) in the defect detection of the fasteners. Our system cascades three DCNN-based detection stages in a coarse-to-fine manner, including two detectors to sequentially localize the cantilever joints and their fasteners and a classifier to diagnose the fasteners’ defects. Extensive experiments and comparisons of the defect detection of catenary support devices along the Wuhan–Guangzhou high-speed railway line indicate that the system can achieve a high detection rate with good adaptation and robustness in complex environments. [ABSTRACT FROM AUTHOR]

In depth-based 3D video, the view synthesis distortion (VSD), is generally measured by modeling the effect of texture and depth errors separately. With such a development, it has been referred that the VSD changes monotonically with respect to to both the texture and depth distortions. In this paper, we find that the VSD does not always change monotonically with them by both theoretical analysis and experimental test, when the effect of the texture and depth errors is considered together. Specifically, first, we prove that the VSD is non-monotonic with the texture distortion. That is, the VSD increases with the increasing texture distortion at higher distortion range but conversely decreases with it at lower range. It is different from the general scenario that only considering the effect of the texture errors. We also analytically depict their relationship with low computational cost and identify the turning point at which the change of the VSD is converted. Second, we confirm that the VSD is always monotonic with the depth distortion, which is consistent with the general scenario that only considering the effect of the depth errors. The non-monotonicity property of the VSD can be utilized to improve the viewing performance of 3D video in relevant applications, since a minimal value of the VSD exists at the turning point. We conduct two applications for this purpose. First, it is used to generate the synthesis view of minimal distortion, which achieves 0.51-dB gain of PSNR on average for the tested scenarios. Second, it is used for lossy compression of texture videos in 3D video, which reduces the coding rate by 24% on average for the tested scenarios, meanwhile, keeps the VSD not increased simultaneously. [ABSTRACT FROM PUBLISHER]

Many cloud service providers (CSPs) provide data storage services with datacenters distributed worldwide. These datacenters provide different get/put latencies and unit prices for resource utilization and reservation. Thus, when selecting different CSPs’ datacenters, cloud customers of globally distributed applications (e.g., online social networks) face two challenges: 1) how to allocate data to worldwide datacenters to satisfy application service level objective (SLO) requirements, including both data retrieval latency and availability and2) how to allocate data and reserve resources in datacenters belonging to different CSPs to minimize the payment cost. To handle these challenges, we first model the cost minimization problem under SLO constraints using the integer programming. Due to its NP-hardness, we then introduce our heuristic solution, including a dominant-cost-based data allocation algorithm and an optimal resource reservation algorithm. We further propose three enhancement methods to reduce the payment cost and service latency: 1) coefficient-based data reallocation; 2) multicast-based data transferring; and 3) request redirection-based congestion control. We finally introduce an infrastructure to enable the conduction of the algorithms. Our trace-driven experiments on a supercomputing cluster and on real clouds (i.e., Amazon S3, Windows Azure Storage, and Google Cloud Storage) show the effectiveness of our algorithms for SLO guaranteed services and customer cost minimization. [ABSTRACT FROM AUTHOR]

Online social networks have been one of the most effective platforms for marketing and advertising. Through the “world-of-mouth” exchanges, so-called viral marketing, the influence and product adoption can spread from few key influencers to billions of users in the network. To identify those key influencers, a great amount of work has been devoted for the influence maximization (IM) problem that seeks a set of $k$ seed users that maximize the expected influence. Unfortunately, IM encloses two impractical assumptions: 1) any seed user can be acquired with the same cost and 2) all users are equally interested in the advertisement. In this paper, we propose a new problem, called cost-aware targeted viral marketing ( CTVM), to find the most cost-effective seed users, who can influence the most relevant users to the advertisement. Since CTVM is NP-hard, we design an efficient (1- 1/\sqrt {e}-\epsilon ) -approximation algorithm, named Billion-scale Cost-award Targeted algorithm (BCT), to solve the problem in billion-scale networks. Comparing with IM algorithms, we show that BCT is both theoretically and experimentally faster than the state-of-the-arts while providing better solution quality. Moreover, we prove that under the linear threshold model, BCT is the first sub-linear time algorithm for CTVM (and IM) in dense networks. We carry a comprehensive set of experiments on various real-networks with sizes up to several billion edges in diverse disciplines to show the absolute superiority of BCT on both CTVM and IM domains. Experiments on Twitter data set, containing 1.46 billions of social relations and 106 millions tweets, show that BCT can identify key influencers in trending topics in only few minutes. [ABSTRACT FROM AUTHOR]

Active state tracking is needed in object classification, target tracking, medical diagnosis, and estimation of sparse signals among other various applications. Herein, active state tracking of a discrete-time, finite-state Markov chain is considered. Noisy Gaussian observations are dynamically collected by exerting appropriate control over their information content, while incurring a related sensing cost. The objective is to devise sensing strategies to optimize the tradeoff between tracking performance and sensing cost. A recently proposed Kalman–like estimator by Zois et al. is employed for state tracking. The associated mean–squared error and a generic sensing cost metric are then used in a partially observable Markov decision process formulation, and the optimal sensing strategy is derived via a dynamic programming recursion. The resulting recursion proves to be nonlinear, challenging control policy design. For two-state systems with scalar measurements, properties of the related cost functions are derived and sufficient conditions are provided regarding the structure of the optimal control policy enabling characterization of when passive state tracking is optimal. To overcome the associated computational burden of the optimal sensing strategy, two lower complexity strategies are proposed, which apply to $n$–state systems with vector measurements. The performance of the proposed strategies is illustrated in a wireless body sensing application, where cost savings as high as $70\%$ are demonstrated for a $3\%$ detection error with respect to a static equal allocation sensing strategy. [ABSTRACT FROM PUBLISHER]

This paper presents a novel virtual prototyping routine for power electronic converter systems. The approach facilitates a comprehensive and systematic benchmarking of different converter concepts based on a multiobjective optimization regarding the efficiency, power density, and costs. The underlying modeling framework is based on detailed and experimentally verified models. In particular, novel cost data as well as unpublished switching loss and core loss measurements are incorporated. The proposed virtual prototyping routine is employed to carry out a comparative study of the potential of Si and SiC semiconductors in a 10-kW residential three-phase photovoltaic inverter application. For this purpose, a state-of-the-art hard-switched three-level Si insulated-gate bipolar transistor (IGBT) system is compared to a hard-switched and to a soft-switched two-level SiC MOSFET system. The candidate systems for each concept are selected among the \boldsymbol \eta-\boldsymbol \rho -\boldsymbol \sigma Pareto-optimized designs based on the life cycle costs. The hard-switched two-level SiC candidate system is found to be the most attractive solution featuring the lowest life cycle costs. When compared to the Si-based candidate system, not only a better power density and efficiency result. At the same time, besides the lower life cycle costs ( $-$22%), lower component costs ( $-$5%) can also be attained. The attractiveness of the found SiC solution is underlined by the simple control and the lowest component count among all concepts. [ABSTRACT FROM AUTHOR]

Cost computation, cost aggregation, disparity optimization, and disparity refinement are the four main steps for stereo matching. While the first three steps have been widely investigated, few efforts have been taken on disparity refinement. In this paper, we propose a color image-guided disparity refinement method to further remove the boundary-inconsistent regions on disparity map. First, the origins of boundary-inconsistent regions are analyzed. Then, these regions are detected with the proposed hybrid-superpixel-based strategy. Finally, the detected boundary-inconsistent regions are refined by a modified weighted median filtering method. Experimental results on various stereo matching conditions validate the effectiveness of the proposed method. Furthermore, depth maps obtained by active depth acquisition devices like Kinect can also be well refined with our proposed method. [ABSTRACT FROM PUBLISHER]

Modular design has recently emerged as an efficient solution to build large data center (DC) facilities. Modular DCs are based on stand-alone prefabricated modules (i.e., PODs) that can be easily installed and interconnected. PODs can generate a large amount of traffic and thus require an ultra-high-capacity interconnection network. However, current electronic and optical interconnect architectures applied to modular DCs may experience major scalability problems in terms of high energy consumption and cabling complexity. To address these problems, we investigate five optical interconnect architectures based on spatial division multiplexing (SDM), and for each architecture, we propose a resource allocation strategy. We also present an extensive comparison among the SDM architectures in terms of cost and performance (i.e., blocking probability and throughput), with the objective to find the architecture offering the best trade-off between cost and performance for given DC sizes and traffic load values. Our results demonstrate that, in small modular DCs with low traffic load, an architecture based only on SDM is the best option, while in medium DCs with medium traffic load, an architecture based on coupled SDM and flexgrid wavelength division multiplexing (WDM) with spectral flexibility is the best solution. Finally, for large DCs with high traffic load values, the best trade-off between cost and performance is achieved by an SDM architecture that is based on uncoupled SDM and flexgrid WDM. [ABSTRACT FROM PUBLISHER]

During at-speed test of high performance sequential ICs using scan-based Logic BIST, the IC activity factor (AF) induced by the applied test vectors is significantly higher than that experienced during its in field operation. Consequently, power droop (PD) may take place during both shift and capture phases, which will slow down the circuit under test (CUT) signal transitions. At capture, this phenomenon is likely to be erroneously recognized as due to delay faults. As a result, a false test fail may be generated, with consequent increase in yield loss. In this paper, we propose two approaches to reduce the PD generated at capture during at-speed test of sequential circuits with scan-based Logic BIST using the Launch-On-Shift scheme. Both approaches increase the correlation between adjacent bits of the scan chains with respect to conventional scan-based LBIST. This way, the AF of the scan chains at capture is reduced. Consequently, the AF of the CUT at capture, thus the PD at capture, is also reduced compared to conventional scan-based LBIST. The former approach, hereinafter referred to as Low-Cost Approach (LCA), enables a 50 percent reduction in the worst case magnitude of PD during conventional logic BIST. It requires a small cost in terms of area overhead (of approximately 1.5 percent on average), and it does not increase the number of test vectors over the conventional scan-based LBIST to achieve the same Fault Coverage (FC). Moreover, compared to three recent alternative solutions, LCA features a comparable AF in the scan chains at capture, while requiring lower test time and area overhead. The second approach, hereinafter referred to as High-Reduction Approach (HRA), enables scalable PD reductions at capture of up to 87 percent, with limited additional costs in terms of area overhead and number of required test vectors for a given target FC, over our LCA approach. Particularly, compared to two of the three recent alternative solutions mentioned above, HRA enables a significantly lower AF in the scan chains during the application of test vectors, while requiring either a comparable area overhead or a significantly lower test time. Compared to the remaining alternative solutions mentioned above, HRA enables a similar AF in the scan chains at capture (approximately 90 percent lower than conventional scan-based LBIST), while requiring a significantly lower test time (approximately 4.87 times on average lower number of test vectors) and comparable area overhead (of approximately 1.9 percent on average). [ABSTRACT FROM AUTHOR]

We propose a new optimal data placement technique to improve the performance of MapReduce in cloud data centers by considering not only the data locality but also the global data access costs. We first conducted an analytical and experimental study to identify the performance issues of MapReduce in data centers and to show that MapReduce tasks that are involved in unexpected remote data access have much greater communication costs and execution time, and can significantly deteriorate the overall performance. Next, we formulated the problem of optimal data placement and proposed a generative model to minimize global data access cost in data centers and showed that the optimal data placement problem is NP-hard. To solve the optimal data placement problem, we propose a topology-aware heuristic algorithm by first constructing a replica-balanced distribution tree for the abstract tree structure, and then building a replica-similarity distribution tree for detail tree construction, to construct an optimal replica distribution tree. The experimental results demonstrated that our optimal data placement approach can improve the performance of MapReduce with lower communication and computation costs by effectively minimizing global data access costs, more specifically reducing unexpected remote data access. [ABSTRACT FROM PUBLISHER]

Wireless sensor networks (WSNs) have been considered to be the next generation paradigm of structural health monitoring (SHM) systems due to the low cost, high scalability and ease of deployment. Due to the intrinsically energy-intensive nature of the sensor nodes in SHM application, it is highly preferable that they can be divided into subsets and take turns to monitor the condition of a structure. This approach is generally called as ‘coverage-preserving scheduling’ and has been widely adopted in existing WSN applications. The problem of partitioning the nodes into subsets is generally called as the ’maximum lifetime coverage problem (MLCP)’. However, existing solutions to the MLCP cannot be directly applied to SHM application. As compared to other WSN applications, we cannot define a specific coverage area independently for each sensor node in SHM, which is however the basic assumption in all existing solutions to the MLCP. In this paper, we proposed two approaches to solve the MLCP in SHM. The performance of the methods is demonstrated through both extensive simulations and real experiments. [ABSTRACT FROM PUBLISHER]

With the popularization of electric vehicles (EVs), the influence on power systems is receiving increasing attention. According to statistics, EVs are in a parking state for more than 90% of the time, thus, the electric energy of the EVs accumulated by aggregators can be used to support the operation of power systems. This paper takes reducing operation costs as the objective function and considers the constraints of EVs and power systems to establish the optimal charging/discharging control of electric vehicles, which meet the operational safety of power systems and convenient driving. The influence on operation costs, peak load, and loss for the proposed charging/discharging two-way control, charging-only control, and free charging modes of EVs are compared. The influence of battery charging and discharging efficiency on these results is also discussed in this paper. [ABSTRACT FROM AUTHOR]

This paper describes the development and evaluation of a new beamforming strategy based on pixel-based focusing for ultrasound linear array systems. We first implement conventional pixel-based beamforming in which the transmitted wave is assumed as spherical and diverging from the centre of the transmit subaperture. This assumed wave-shape is only valid within a limited angle on each side of the beam and this restricts the number of different subaperture positions from which data can be combined to improve image quality. By analyzing the field patterns, we propose a new unified pixel-based beamforming algorithm that better adapts to the non-spherical wave-shape of the transmit beam. This approach enables us to select the best-possible signal from each transducer waveform for data superposition. In simulations and a phantom study, we show that the unified pixel-based beamformer offers significant improvements in image quality compared to other delay-and-sum methods but at a higher computational cost. The new algorithm also demonstrates robust performance in a limited in vivo study. Overall, the results show that it is potentially of value in clinical applications. [ABSTRACT FROM AUTHOR]