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As a larger elderly human population is expected worldwide in the next 30 years, the occurrence of aging-associated illnesses will also be increased. The use of prosthetic devices by this population is currently important and will be even more dramatic in the near future. Hence, the design of prosthetic devices able to reduce some of the problems associated with the use of current components, such as stress shielding, reduced mobility, infection, discomfort, etc., becomes relevant. The use of additive manufacturing (AM) and the design fabrication of self-supported cellular structures in the biomedical area have opened up important opportunities for controlling the physical and mechanical properties of hip implants, resulting in specific benefits for the patients. Different studies have reported the development of hip prosthetic designs employing AM, although there are still opportunities for improvement when it comes to customized design and tuning of the physical and mechanical properties of such implants. This work shows the design and manufacture by AM of a personalized stainless-steel partial hip implant using tomography data and self-supported triply periodic minimal surface (TPMS) cell structures; the design considers dimensional criteria established by international standards. By employing tomography data, the external dimensions of the implant were established and the bone density of a specific patient was calculated; the density and mechanical properties in compression of the implant were modulated by employing an internal gyroid-type cell structure. Using such a cell structure, the patient's bone density was emulated; also, the mechanical properties of the implant were fine-tuned in order to make them comparable to those reported for the bone tissue replaced by the prosthesis. The implant design and manufacturing methodology developed in this work considered the clinical condition of a specific patient and can be reproduced and adjusted for different types of bone tissue qualities for specific clinical requirements.

Commercial space transportation is becoming more affordable and accessible. Consequently, we expect to see significant expansion of commercial space launch activities in the coming decade. As space vehicles travel through airspace during the launch and re-entry stages, they potentially disrupt the regular operations of traditional users. This paper estimates the potential economic and operational impacts of commercial space horizontal launch activities on airlines under various launch scenarios using predictive fast-time simulation modeling, focusing on Cecil Air and Space Port in Jacksonville (Florida) and the rules governing the national airspace system (NAS) in the United States. Our results indicate that the existing 4-hour airspace closure rule impacts a significant number of flights, resulting in flight time delays, additional flight distance and fuel burn, as well as other direct operating costs. Safely reducing the duration of airspace closures could serve as a simple solution to mitigate the impacts on airlines and other traditional NAS users. More importantly, treating our studied launch vehicle as an aircraft and opening its departure/arrival corridor to air traffic during a horizontal launch and return would potentially reduce the impacts on airlines significantly, depending on the location of the spaceport, planned flight paths and the trajectory of the launch.

One of the current problems in engineering is the collapse of bridges that cross rivers due to the scour of their piers. This can lead to collapse and thus high costs associated with reconstruction. One way of avoiding bridge collapses is the installation of an arch-shaped superstructure that goes over the bridge, which by means of hangers lifts the deck to replace the damaged piers. A problem arises when the hangers are tight since keeping the properties of the deck of the original bridge is mandatory in order to avoid serious damages to the bridge. This paper proposes the use of algorithms with great performance in the literature, which are Black Hole and cuckoo search, to find the correct order to tight the hangers as well as the magnitude that minimizes the difference in stresses along the concrete with respect to the original bridge. Finally, the results associated with various real instances are presented according to the San Luis bridge, where the proper functioning of the Black Hole Algorithm can be observed, as opposed to cuckoo search and 4 other techniques. This implies that the use of metaheuristic techniques presents acceptable results in reasonable calculation times, validating the implementation of these techniques for this problem.

Understanding the challenges of implementing blockchain solutions is an important step toward scaling and adopting the technology. This paper analyses the adoption of blockchain technology in the management of academic certificates. In this use case, we identify certification providers that have adopted a permissionless approach and consortiums of academic institutions that are in the process of building permissioned networks. We explore the challenges faced by both approaches and obtain information from competing projects to provide a preliminary approach for cost-benefit analysis that could potentially be applied for similar blockchain projects. For the management of academic certificates, we find that beyond the cost of implementing the technology there are additional elements of critical importance for adoption. For example, if blockchain-enabled certificates will replace notarized documents, how do they complement other forms of digital credentials, the ease of integration to existing administrative records within institutions and if they are a viable first step toward a more comprehensive, efficient and reliable systems to share information among institutions.

Con el creciente aumento de la tecnología, la industria de la comunicación a través de internet se hace cada vez más necesaria utilizarla en la familia, el trabajo incluso en los estudios, de tal manera que es indispensable mantener una conexión estable. Pero debido al confinamiento que se vive a principios del año 2020, por causa de la Pandemia de Coronavirus declarada por la Organización Mundial de la Salud, lo cual, ha ocasionado un aumento en la cantidad de usuarios conectados, ocasionando inestabilidad en el servicio. Por ello, se realizó este estudio iniciando con una encuesta a 180 usuarios con servicio contratado de 5 megas. Con la ayuda de herramientas de control de calidad, como los gráficos de control X S, se evidenció que de 9 am hasta las 6 pm, son las horas donde se generaba mayor fluctuación de la conexión, pues el servicio presentó inestabilidad y desciende alrededor de los 3.5 megas. Al aplicar el Diagrama de Ishikawa se encontraron una serie de causas que generaban dicho problema. Posteriormente, se realizó un graficó mediante un Diagrama de Pareto, donde se evidenció que aproximadamente el 80% de los problemas son ocasionados por sobre uso del servicio, falta de mantenimiento y materiales de mala calidad.

Currently, industry is undergoing an exponential increase in binary-based combinatorial problems. In this regard, metaheuristics have been a common trend in the field in order to design approaches to successfully solve them. Thus, a well-known strategy includes the employment of continuous swarm-based algorithms transformed to perform in binary environments. In this work, we propose a hybrid approach that contains discrete smartly adapted population-based strategies to efficiently tackle binary-based problems. The proposed approach employs a reinforcement learning technique, known as SARSA (State–Action–Reward–State–Action), in order to utilize knowledge based on the run time. In order to test the viability and competitiveness of our proposal, we compare discrete state-of-the-art algorithms smartly assisted by SARSA. Finally, we illustrate interesting results where the proposed hybrid outperforms other approaches, thus, providing a novel option to tackle these types of problems in industry.

The idea of hybrid approaches have become a powerful strategy for tackling several complex optimisation problems. In this regard, the present work is concerned with contributing with a novel optimisation framework, named learning-based linear balancer (LB2). A regression model is designed, with the objective to predict better movements for the approach and improve the performance. The main idea is to balance the intensification and diversification performed by the hybrid model in an online-fashion. In this paper, we employ movement operators of a spotted hyena optimiser, a modern algorithm which has proved to yield good results in the literature. In order to test the performance of our hybrid approach, we solve 15 benchmark functions, composed of unimodal, multimodal, and mutimodal functions with fixed dimension. Additionally, regarding the competitiveness, we carry out a comparison against state-of-the-art algorithms, and the sequential parameter optimisation procedure, which is part of multiple successful tuning methods proposed in the literature. Finally, we compare against the traditional implementation of a spotted hyena optimiser and a neural network approach, the respective statistical analysis is carried out. We illustrate experimental results, where we obtain interesting performance and robustness, which allows us to conclude that our hybrid approach is a competitive alternative in the optimisation field.

One of the central issues that must be resolved for a metaheuristic optimization process to work well is the dilemma of the balance between exploration and exploitation. The metaheuristics (MH) that achieved this balance can be called balanced MH, where a Q-Learning (QL) integration framework was proposed for the selection of metaheuristic operators conducive to this balance, particularly the selection of binarization schemes when a continuous metaheuristic solves binary combinatorial problems. In this work the use of this framework is extended to other recent metaheuristics, demonstrating that the integration of QL in the selection of operators improves the exploration-exploitation balance. Specifically, the Whale Optimization Algorithm and the Sine-Cosine Algorithm are tested by solving the Set Covering Problem, showing statistical improvements in this balance and in the quality of the solutions.

In complexity theory, there is a widely studied grouping of optimization problems that belongs to the non-deterministic polynomial-time hard set. One of them is the set covering problem, known as one of Karp’s 21 $${\mathscr {NP}}$$ -complete problems, and it consists of finding a subset of decision variables for satisfying a set of constraints at the minimum feasible cost. However, due to the nature of the problem, this cannot be solved using traditional complete algorithms for hard instances. In this work, we present an improved binary version of the monkey search algorithm for solving the set covering problem. Originally, this approximate method was naturally inspired by the cognitive behavior of monkeys for climbing mountains. We propose a new climbing process with a better exploratory capability and a new cooperation procedure to reduce the number of unfeasible solutions. For testing this approach, we present a detailed computational results section, where we illustrate how this variation of the monkey search algorithm is capable of reaching various global optimums for a well-known instance set from the Beasley’s OR-Library and how it outperforms many other heuristics and meta-heuristics addressed in the literature. Moreover, we add a complete statistical analysis to show the effectiveness of the proposed approach with respect to the original version.

Using the approximate algorithms, we are faced with the problem of determining the appropriate values of their input parameters, which is always a complex task and is considered an optimization problem. In this context, incorporating online control parameters is a very interesting issue. The aim is to vary the parameters during the run so that the studied algorithm can provide the best convergence rate and, thus, achieve the best performance. In this paper, we compare the performance of a self-adaptive approach for the biogeography-based optimization algorithm using the mutation rate parameter with respect to its original version and other heuristics. This work proposes altering some parameters of the metaheuristic according to its exhibited efficiency. To test this approach, we solve the set covering problem, which is a classical optimization benchmark with many industrial applications such as line balancing production, crew scheduling, service installation, databases, among several others. We illustrate encouraging experimental results, where the proposed approach is capable of reaching various global optimums for a well-known instance set taken from the Beasleys OR-Library, and sometimes, it improves the results obtained by the original version of the algorithm.

We provide a common framework that relates traditional event study estimation methods in finance to a modern approach for causal event studies. The framework provides a model for abnormal returns that nests the fitted market model (the traditional approach) and more recent approaches based on difference-in-differences and synthetic control methods. We show that a synthetic control method in this context can be understood as a synthetic portfolio. We provide a simulation exercise and an empirical application, using mergers and acquisitions as the event of interest, to evaluate the performance of the different models within the framework. Our results indicate that causal inference methods such as synthetic matching or difference-in-differences do not provide an improvement over the traditional approach based on the fitted market model. Although the fitted market model may not always abide by the conditions under which it is considered a proper counterfactual, its performance indicates that it is still a good potential outcome.

The manufacturing Cell Design Problem (MCDP) is a classical optimization problem that finds application in lines of manufacture. The problem consist in distributing machines in cells, where the parts processed by each machine travels in the production process in such a way that productivity is improved. To solve the MCDP we employ a novel metaheuristic, which is inspired by actions, attitudes, and conducts that people normally have in life, named Human behavior-based optimization (HBBO). An individual try to evolve in life by trying his best in order to be a better human being with a brilliant future, successful at life, and be an example for others. We couple the HBBO with Autonomous Search (AS), which allows the modification of internal components of our approach when exposed to changing external forces and opportunities. We compare our HBBO-AS with the classic HBBO and an implementation using IRace, which is a software package that allows us to automatize the configuration of an algorithm through automatic configuration procedures. Additionally, in order to test the competitiveness of our results, we compare with other algorithms proved to perform well solving the MCDP. We illustrate experimental results, where the proposed approach is able to obtain interesting performance and robustness in the 125 well-known instances of the MCDP.

This paper presents the investigation about a problem situation that Electric Distributor Companies are facing in Chile resulting from transit accidents. The number of vehicle crashes to power distribution poles and street lighting has grown. This situation causes discomfort to citizen and mainly to the neighbors due to power cuts and even on occasion , losses of human lives because of the accident that have occurred. Based on previous research, the accidents are not random nor chance dependent, but the majority of transit accident follow parameters or variables from the scenery where it occurs. In order to analyze the variables and the degree this variables affect the accidents, a model of Perceptron and Multipercetron Artificial Neural Networks and a Multiple Nonlinear Regression model are proposed. An empirical study was made; collecting data from a distributor company and from Chilean National Traffic Safety Commission, where the more frequent variables involved in accidents were determined to develop the mentioned models. These variables were investigated and also their influence on the occurrence of vehicle crashes to power distribution poles could be confirmed. With this data, the prediction of post crashes was developed, where through the application of the neural network and multiple nonlinear regression, revealed 95.7% of acceptable predictions. This study will bring benefits to power distribution companies considering a risk index in the streets, based on the number of crashes of poles per street; this will allow optimal decisions in future electrical distribution projects avoiding critical areas.

In this paper, we integrate the autonomous search paradigm on a swarm intelligence algorithm in order to incorporate the auto-adjust capability on parameter values during the run. We propose an independent procedure that begins to work when it detects a stagnation in a local optimum, and it can be applied to any population-based algorithms. For that, we employ the autonomous search technique which allows solvers to automatically re-configure its solving parameters for enhancing the process when poor performances are detected. This feature is dramatically crucial when swarm intelligence methods are developed and tested. Finding the best parameter values that generate the best results is known as an optimization problem itself. For that, we evaluate the behavior of the population size to autonomously be adapted and controlled during the solving time according to the requirements of the problem. The proposal is testing on the dolphin echolocation algorithm which is a recent swarm intelligence algorithm based on the dolphin feature to navigate underwater and identify prey. As an optimization problem to solve, we test a machine-part cell formation problem which is a widely used technique for improving production flexibility, efficiency, and cost reduction in the manufacturing industry decomposing a manufacturing plant in a set of clusters called cells. The goal is to design a cell layout in such a way that the need for moving parts from one cell to another is minimized. Using statistical non-parametric tests, we demonstrate that the proposed approach efficiently solves 160 well-known cell manufacturing instances outperforming the classic optimization algorithm as well as other approaches reported in the literature, while keeping excellent robustness levels.

In the last few years, wireless communication systems have demonstrated the potential to achieve high-speed data transmission by leveraging a large contiguous spectrum and wide-bandwidth electronic/photonic components. This led to the proposed concept of THz-wireless fiber extenders, an approach in which the THz-wireless link is embedded in existing fiber-based infrastructure to provide an alternative solution for areas where the installation of fibers is not economic or technically not feasible. To demonstrate the potential of this hybrid THz-wireless / fiber-optical approach, we have set up two long-range high-speed THz wireless links. The first Line-of-Sight (LoS) link bridges a distance of 500 meters and the second one stretches over 1 kilometer. Using MMIC-based THz frontends in the 300 GHz band, employing a single-polarization approach, and assuming an SD-FEC threshold corresponding to a 25% overhead, we were able to transmit a maximum net data rate of 76 Gb/s (16-QAM) and of 44 Gb/s (4-QAM) over the 500 m and 1 km LoS link, respectively. Moreover, we have started to investigate the effect of weather conditions on the transmission performance. In this context, we have observed the Q-factor over 10 hours of continuous operation.

Over the last 10 years, researchers have shown that paper is a promising substrate for affordable biosensors. The field of paper-microfluidics has evolved rapidly in that time, with simple colorimetric assays giving way to more complex electrochemical devices that can handle multiple samples at a given time. As paper devices become more complex, the ability to precisely control different fluids simultaneously becomes a challenge. Specifically, automated flow control is a necessary attribute to make paper-based devices more useable in resource-limited settings. Flow control strategies on paper are typically developed experimentally through trial-and-error, with little focus on theory. This is because flow behavior in paper is not well understood and sometimes difficult to predict precisely. Additionally, popular theoretical models are too simplistic, making them unsuitable for complex device designs and application conditions. A better understanding of flow theory would allow devices conceived straight from theoretical models. This could save time and resources by reducing experimental work. In this review, we provide an overview of different theoretical models used to characterize imbibition in paper substrates and document the latest flow control strategies that have been applied to automated fluid control on paper. Additionally, we look at current efforts to commercialize paper-based devices along with challenges facing this industry.

The need to make good use of resources has allowed metaheuristics to become a tool to achieve this goal. There are a number of complex problems to solve, among which is the Set-Covering Problem, which is a representation of a type of combinatorial optimization problem, which has been applied to several real industrial problems. We use a binary version of the optimization algorithm based on teaching and learning to solve the problem, incorporating various binarization schemes, in order to solve the binary problem. In this paper, several binarization techniques are implemented in the teaching/learning based optimization algorithm, which presents only the minimum parameters to be configured such as the population and number of iterations to be evaluated. The performance of metaheuristic was evaluated through 65 benchmark instances. The results obtained are promising compared to those found in the literature.

On line; International audience; Satisfiability modulo theory (SMT) solving strategies are composed of various components and parameters that can dramatically affect the performance of an SMT solver. Each of these elements includes a huge amount of options that cannot be exploited without expert knowledge. In this work, we analyze separately the different strategy components of the Z3 theorem prover, which is one of the most important solvers of the SMT community. We propose some rules for modifying components, parameters, and structures of solving strategies. Using these rules inside different engines leads to an automated strategy learning process which does not require any end‐user expert knowledge to generate optimized strategies. Our algorithms and rules are validated by optimizing some solving strategies for some selected SMT logics. These strategies are then tested for solving some SMT library benchmarks issued from the SMT competitions. The strategies we automatically generated turn out to be veryefficient.

[EN] The reaction times of individuals over consecutive visual stimuli have been studied using an entropy-based model and a failure machinery approach. The used tools include the fast Fourier transform and a spectral entropy analysis. The results indicate that the reaction times produced by the independently responding individuals to visual stimuli appear to be correlated. The spectral analysis and the entropy of the spectrum yield that there are features of similarity in the response times of each participant and among them. Furthermore, the analysis of the mistakes made by the participants during the reaction time experiments concluded that they follow a behavior which is consistent with the MTBF (Mean Time Between Failures) model, widely used in industry for the predictive diagnosis of electrical machines and equipment., This research was partially supported by grant no. RTI2018-102256-B-I00 (Spain).

We demonstrate the real-time transmission of a 34-GBd PDM-QPSK signal over two fiber-optic links interconnected by a THz wireless fiber extender at 300 GHz carrier frequency, with joint impairment compensation by a single-carrier DSP.

This research seeks to design, implement, and test a fully automatic high-frequency trading system that operates on the Chilean stock market, so that it is able to generate positive net returns over time. A system that implements high-frequency trading (HFT) is presented through advanced computer tools as an NP-Complete type problem in which it is necessary to optimize the profitability of stock purchase and sale operations. The research performs individual tests of the algorithms implemented, reviewing the theoretical net return (profitability) that can be applied on the last day, month, and semester of real market data. Finally, the research determines which of the variants of the implemented system performs best, using the net returns as a basis for comparison. The use of particle swarm optimization as an optimization algorithm is shown to be an effective solution since it is able to optimize a set of disparate variables but is bounded to a specific domain, resulting in substantial improvement in the final solution.

In this paper, we evaluate the transmission performance of a real-time 100 Gb/s digital-coherent THz wireless link, operating on a 300-GHz carrier over a LoS distance of 0.5 m in a 2x2 MIMO configuration. Using a 100 Gb/s Ethernet traffic generator, we evaluate throughput, frame losses and latency for different frame sizes and payloads. The link shows a stable long-term performance as reported by pre-FEC BER measurements taken from the real-time modem's DSP over a period of 96 hours.

There are several works using computer vision to create databases with geotagged elements from urban areas, however, most of them are based on existing images provided by a third-party. In order to maintain up-to-date information that helps governmental, educational, economical, and private sectors decide how to invest resources, it is essential to be capable of acquiring the data on-demand. This research proposes building a prototype to capture street-level videos, using deep learning to detect the relevant objects, employing computer vision algorithms to track them, and applying least-squares to calculate their geotags. Experimental results on the object detection task using a Faster-RCNN model are carried out on a dataset of real urban imagery taken by the prototype in the city of Culiacan, showing positive results despite the high variety of appearances within objects of the same class and the challenging visual conditions of the urban scenery.

Multicore fiber (MCF) transmission is considered as one of the promising technologies for breaking the capacity limit of traditional single mode fibers (SMFs). Managing the XT and configuring optical paths adaptively based on the XT are important as well as achieving longer-distance and larger-capacity transmission, because inter-core crosstalk (XT) could be the main limiting factor for MCF transmission. In a real MCF network, the inter-core XT in a particular core is likely to change continuously as the optical paths in the adjacent cores are dynamically assigned to match the dynamic nature of the data traffic. If we configure the optical paths while ignoring the inter-core XT value, the Q-factors may become excessive. Therefore, monitoring the inter-core XT value continuously and configuring optical path parameters adaptively and flexibly are essential. To address these challenges, we develop an MCF transport network testbed and demonstrate an XT-aware traffic engineering scenario. With the help of a software-defined network (SDN) controller, the modulation format and optical path route are adaptively changed based on the monitored XT values by using programmable devices such as a real-time transponder and a reconfigurable optical add-drop multiplexer (ROADM).

Within the category of project scheduling problems, there is a specific problem within the software industry referred to as the software project scheduling problem. The problem consists in the correct allocation of employees to the different tasks that make up a software project, bearing in mind time and cost restraints. To achieve this goal, the present work first uses metaheuristic intelligent water drops illustrating; this is a recent stochastic swarm-based method increasingly used for solving optimization problems. Finally, the results and comparisons with experiments performed with other techniques are presented, demonstrating the solidity of the approach presented.

Bio-inspired computing is an engaging area of artificial intelligence which studies how natural phenomena provide a rich source of inspiration in the design of smart procedures able to become powerful algorithms. Many of these procedures have been successfully used in classification, prediction, and optimization problems. Swarm intelligence methods are a kind of bio-inspired algorithm that have been shown to be impressive optimization solvers for a long time. However, for these algorithms to reach their maximum performance, the proper setting of the initial parameters by an expert user is required. This task is extremely comprehensive and it must be done in a previous phase of the search process. Different online methods have been developed to support swarm intelligence techniques, however, this issue remains an open challenge. In this paper, we propose a hybrid approach that allows adjusting the parameters based on a state deducted by the swarm intelligence algorithm. The state deduction is determined by the classification of a chain of observations using the hidden Markov model. The results show that our proposal exhibits good performance compared to the original version.

Metaheuristics are multi-purpose problem solvers devoted to particularly tackle large instances of complex optimization problems. However, in spite of the relevance of metaheuristics in the optimization world, their proper design and implementation to reach optimal solutions isnot a simple task. Metaheuristics require an initial parameter configuration, which is dramatically relevant for the efficient exploration and exploitation of the search space, and therefore to the effective finding of high-quality solutions. In this paper, the authors propose a variation of the water cycle inspired metaheuristic capable of automatically adjusting its parameter by using the autonomous search paradigm. The goal of our proposal is to explore and to exploit promising regions of the search space to rapidly converge to optimal solutions. To validate the proposal, we tested 160 instances of the manufacturing cell design problem, which is a relevant problem for the industry, whose objective is to minimize the number of movements and exchanges of parts between organizational elements called cells. As a result of the experimental analysis, the authors checked that the proposal performs similarly to the default approach, but without being specifically configured for solving the problem. Ricardo Soto is supported by Grant CONICYT/FONDECYT/REGULAR/1190129. Broderick Crawford is supported by Grant CONICYT/FONDECYT/REGULAR/1171243. Rodrigo Olivares is supported by Postgraduate Grant Pontificia Universidad Católica de Valparaíso (INF-PUCV 2015).

Universal Plug and Play (UPnP) communication protocol has become in a standard for communication between devices in the Internet of Things (IoT). UPnP protocol has facilitated the increased use of IoT devices integrating in activities of people and also in different processes of organizations. A disadvantage of UPnP protocol is that it operates in conjunction with other standardized network communications protocols that make it vulnerable and target of threats that can cause a security incident.Files in XML format, which UPnP protocol uses for identification of devices in a local area network, have a standard structure and when transmitted in clear text represents a security risk that may compromise confidentiality, integrity and availability of devices across the network. This article presents the proposal of a security tool using a Raspberry Pi2 programmed in Python that modifies information of XML files when they are transmitted through UPnP protocol to provide confidentiality to identity of IoT devices and it was taken as a case of study to Smart TV for development of tests of this work. Results obtained when using security tool and Smart TVs showed that communication is functional in a private data network, when they use UPnP protocol with modified XML file.

The project scheduling problem has been widely studied given the practical importance it has and the complexity to find efficient solutions. In this work we address an extension of the same problem based on the Resource-Constrained Multi-Project Scheduling Problem, taking into account that each project has a set of own resources available for consumption. We propose a hybrid algorithm based on Ant Colony Optimization and a Local Search procedure based on Hill-Climbing First Improvement supported by an SMT Solver as a movement satisfaction verifier. Our approach was tested with a set of 12 instances belonging to the MPSPLIB datasets. We have obtained near-optimal solutions for several instances of the problem.

Providing energy to areas isolated from the electricity grid through the use of a smart integrated renewable energy system (SIRES) is based on the Agenda 2030 for Sustainable Development, specifically Goal 7, according to the United Nations (UN). This study analyzes the process of identifying the appropriate size of a SIRES considering technical and economic factors (cost of energy-COE). An optimization integer linear programming model proposed was used, and a subsequent spatial-temporal analysis of the different variables was conducted. The model comprises locally available renewable energy resources, such as biomass, biogas, wind power, solar photovoltaic, and thermal power. Furthermore, it could be used to determine the energy potential of each of the isolated villages, identifying those areas in which the SIRES could be implemented as a sustainable and economical solution. The design simulates the cost of the initial investment and energy generation in the chosen village. The study also includes the study case in the region of Valparaiso, Chile. Different scenarios were used to optimize the available resources and minimize the COE generation.

Many different algorithms have been developed for smoke detection of in video. However, most of these algorithms have been implemented in simulations with specialized software or computers with high computational capacity. This article presents results of implementation of a method to smoke detection in video, which was implemented in a Raspberry Pi embedded computer using OpenCV library. This method proposes to use different smoke characteristics such as: movement, color, direction, growth, accumulation and a background subtraction method, so that movement detection is as optimal as possible to be able to detect early smoke presence and prevent a possible mishap. This algorithm was evaluated using 22 different videos obtaining up to 90.9% accuracy in smoke detection and up to 9.1% false positives, showing that proposed method obtains favorable results.

An artificial neural network multi-layer perceptron classifier was developed to make a diagnosis of Parkinson’s disease (PD) using a dataset obtained from the UCI Machine Learning Repository. The dataset consists of voice recordings from patients with PD and a control group. Multiple networks were trained to vary the number of neurons in the hidden layer between 10 and 6000 in steps of 10. The network with 280 neurons in the hidden layer had the best performance showing an accuracy of 95.23%, a precision of 96.40%, a recall of 97.10%, a specificity of 90.19%, and a F1-score of 96.75%. Artificial neural networks can be used to differentiate if a patient has PD using speech-related features. Furthermore, machine learning methods could predict other neurological diseases if the biomedical information is available.

The use of artificial neural networks (ANNs) has been very helpful in carrying out prediction, classification and data optimization tasks. In this work, ANNs were used to predict the diagnosis for breast cancer in a women population with overweight or obese and possible diabetes, coupled with a pre or postmenopausal stage and compared against other machine learning techniques reported in literature. The algorithms used to train the ANNs models were Scaled Conjugate Gradient, Resilient Backpropagation and Conjugate Gradient Backpropagation with Powell Beale Restarts. The algorithms results were compared with the original dataset creator’s work, as well as other authors using the same dataset for classification task, a better classification was accomplished using this work ANNs. With four predictors the next values were obtained, AUC = 0.96, sensitivity = 0.96, specificity = 0.96 and Youden index = 0.92. With nine predictors the next values were obtained, AUC = 0.96, sensitivity = 0.95, specificity = 0.97 and Youden index = 0.92. Different strategies are suggested to improve the results, exploring more hidden layers and a different validation method.

In this paper, we demonstrate for the first time 100 Gbit/s real-time THz-wireless data transmission at a carrier frequency of 300 GHz using a single broadband real-time modem originally designed for 2×2 Polarization-MIMO digital-coherent fiber-optical transmission. As a reference, the performance of the 50-cm long, LoS, spatially multiplexed (x2) wireless link is also evaluated using offline DSP algorithms optimized for the THz setup. During the real-time operation of the system, we have recorded the pre-FEC BER over 100 hours of continuous operation to demonstrate the stability of the system.

This paper experimentally validates the effectiveness of state-of-the-art single-carrier digital signal processing algorithms used in digital-coherent fibre-optical transmission systems to also compensate for transmission impairments arising from THz wireless links. This is an important step towards the realization of combined fibre-optical / THz-wireless links where a single DSP stage at the receiver is able to correctly decode the signal. Experimental results of a 58 cm-long 16QAM THz link with up to 100 Gb/s net capacity demonstrate the robustness of the fibre-optical DSP algorithms regarding THz transmission at a carrier frequency of 300 GHz.

This paper investigates via simulations the robustness of digital signal processing algorithms used normally in optical communication systems to compensate the combined effect of transmission impairments in optic-THz systems. In addition, we define a novel figure of merit to represent the combined signal-to-noise ratios of the optical link and of the THz link in a single quantity. The simulations are carried out using a 32 GBd m-QAM signal at a frequency of 193.58 THz in the optical domain and at a frequency of 300 GHz in the wireless domain.

The recognition of partial discharge (PD) sources is an important task of the monitoring and diagnostics of high-voltage components. Nowadays, digital PD measuring systems have the capability of extracting features and form scatter plots with such data sets. Part of an unsupervised PD analysis system is to discover clusters within the data sets and link them to particular PD sources. Due to the nature of PD data sets, clusters may appear very close to each other or even merged hindering the separation of sources. Clustering methods based on spatial density such as the density peak clustering (DPC) method and DBSCAN are suitable approaches to discover clusters within PD data sets. However, their accuracy can be reduced due to the proximity among clusters. In this paper, a new method is presented to improve the accuracy of the DPC method. Our method proposes to partition the data set and later pass the resulting subsets to the DPC method. The partitioning is based on the spatial density of data computed by a smoothed density method (SD). SD has the advantage of being fast and not requiring high computational power. As a final step, a routine is applied to group the sub clusters as per the DPC method having a threshold for the data contour distance as a criterion. This method proved higher accuracy to discover clusters in actual PD data sets. However, the threshold for the data contour distance still needs further research.

Hand gesture recognition (HGR) takes a central role in human&ndash, computer interaction, covering a wide range of applications in the automotive sector, consumer electronics, home automation, and others. In recent years, accurate and efficient deep learning models have been proposed for real-time applications. However, the most accurate approaches tend to employ multiple modalities derived from RGB input frames, such as optical flow. This practice limits real-time performance due to intense extra computational cost. In this paper, we avoid the optical flow computation by proposing a real-time hand gesture recognition method based on RGB frames combined with hand segmentation masks. We employ a light-weight semantic segmentation method (FASSD-Net) to boost the accuracy of two efficient HGR methods: Temporal Segment Networks (TSN) and Temporal Shift Modules (TSM). We demonstrate the efficiency of the proposal on our IPN Hand dataset, which includes thirteen different gestures focused on interaction with touchless screens. The experimental results show that our approach significantly overcomes the accuracy of the original TSN and TSM algorithms by keeping real-time performance.

Constraint Programming is a powerful paradigm which allows the resolution of many complex problems, such as scheduling, planning, and configuration. These problems are defined by a set of variables and a set of constraints. Each variable has non-empty domain of possible value and each constraint involves some subset of the variables and specifies the allowable combinations of values for that subset. The resolution of these problems is carried out by a constraint satisfaction solver which explores a search tree of potential solutions. This exploration is controlled by the enumeration strategy, which is responsible for choosing the order in which variables and values are selected to generate the potential solution. There exist different ways to perform this selection, and depending on the quality of this decision, the efficiency of the solving process may dramatically vary. Autonomous search is a particular case of adaptive systems that aims at improving its solving performance by adapting itself to the problem at hand without manual configuration of an expert user. The goal is to improve their solving performance by modifying and adjusting themselves, either by self-adaptation or by supervised adaptation. This approach has been effectively applied to different optimization and satisfaction techniques such as constraint programming, metaheuristics, and SAT. In this paper, we present a new Autonomous Search approach for constraint programming based on four modern bio-inspired metaheuristics. The goal of those metaheuristics is to optimize the self-tuning phase of the constraint programming search process. We illustrate promising results, where the proposed approach is able to efficiently solve several well-known constraint satisfaction problems.

In this paper, we present a novel digital preemphasis algorithm to compensate for the electrical bandwidth limitations at the transceiver also by taking into account the quantization noise introduced by the signal digitalization. The proposed method is based on the minimization of the mean square error between the desired input signals and the output signals of the digital-to-analog converter/analog-to-digital converter (DAC/ADC), when assuming the knowledge of the DAC/ADC frequency responses. Though this paper focuses on the DAC/ADC compensation, the introduced method could be applied to electrical bandwidth limitations caused by any other component within the transponder. The performance of the algorithm is assessed in optical back-to-back configuration by comparing it against the case without digital preemphasis and with a previously published method to compensate for DAC bandwidth limitations. Our analysis shows that when utilizing realistic descriptions of the DAC/ADC, the proposed digital preemphasis (at the transmitter) or digital compensation (at the transmitter and receiver) can considerably increase the maximum transmittable symbol rate for the case of advanced modulation formats. For example, the maximum symbol rate can be ideally increased up to $\sim$ 60% for the case of 16QAM when employing the high-speed DAC with a $-$ 3 dB electrical bandwidth of $\sim$ 16 GHz and with six effective number of bits. Furthermore, we evaluate the impact of additional noise sources, based on experimental measurements, envisioning potential for further improvements of the digital preemphasis module. Finally, we experimentally verified our algorithm, for the specific case of polarization-multiplexed 16QAM, showing a considerable match between simulation and lab results.

Today, it is common for software projects to collect measurement data through development processes. With these data, defect prediction software can try to estimate the defect proneness of a software module, with the objective of assisting and guiding software practitioners. With timely and accurate defect predictions, practitioners can focus their limited testing resources on higher risk areas. This paper reports the results of three empirical studies that uses an automated genetic defect prediction framework. This framework generates and compares different learning schemes (preprocessing + attribute selection + learning algorithms) and selects the best one using a genetic algorithm, with the objective to estimate the defect proneness of a software module. The first empirical study is a performance comparison of our framework with the most important framework of the literature. The second empirical study is a performance and runtime comparison between our framework and an exhaustive framework. The third empirical study is a sensitivity analysis. The last empirical study, is our main contribution in this paper. Performance of the software development defect prediction models (using AUC, Area Under the Curve) was validated using NASA-MDP and PROMISE data sets. Seventeen data sets from NASA-MDP (13) and PROMISE (4) projects were analyzed running a NxM-fold cross-validation. A genetic algorithm was used to select the components of the learning schemes automatically, and to assess and report the results. Our results reported similar performance between frameworks. Our framework reported better runtime than exhaustive framework. Finally, we reported the best configuration according to sensitivity analysis.

Metaheuristics are smart problem solvers devoted to tackling particularly large optimization problems. During the last 20 years, they have largely been used to solve different problems from the academic as well as from the real-world. However, most of them have originally been designed for operating over real domain variables, being necessary to tailor its internal core, for instance, to be effective in a binary space of solutions. Various works have demonstrated that this internal modification, known as binarization, is not a simple task, since the several existing binarization ways may lead to very different results. This of course forces the user to implement and analyze a large list of binarization schemas for reaching good results. In this paper, we explore two efficient clustering methods, namely KMeans and DBscan to alter a metaheuristic in order to improve it, and thus do not require on the knowledge of an expert user for identifying which binarization strategy works better during the run. Both techniques have widely been applied to solve clustering problems, allowing us to exploit useful information gathered during the search to efficiently control and improve the binarization process. We integrate those techniques to a recent metaheuristic called Crow Search, and we conduct experiments where KMeans and DBscan are contrasted to 32 different binarization methods. The results show that the proposed approaches outperform most of the binarization strategies for a large list of well-known optimization instances.

The abundance of online media content requires highly scalable architectures to allow cross-media monitoring. This paper presents an innovative big data-as-a-service platform for analysing large complex networks in order to enhance cross-media monitoring. In contrast to the existing media monitoring systems, the platform equips marketers with several distinctive features. First, while most of the systems perform quantitative exploratory analysis of social media, our platform applies graph analytics in order to reveal social interaction types, hidden patterns in the cross-media network and the information diffusion over time. Second, our platform integrates and implements distributed versions of graph analytics algorithms (Louvain, HITS and others) that can scale to a large volume of data. Third, the creation of cross-media graphs is triggered by user-defined queries that can be easily specified by marketers. Thus, end-users can build and analyse different graphs according to specific goals of the study. Finally, the platform allows reducing Hadoop cluster usage costs due to executing the graph mining algorithms on demand triggered by user-defined queries. Instead of running costly streaming processes that continuously listen for new queries, we implemented Spark-as-a-service approach via Apache Livy REST interface.

Evolutionary algorithms have been used to solve several optimization problems, showing an efficient performance. Nevertheless, when these algorithms are applied they present the difficulty to decide on the appropriate values of their parameters. Typically, parameters are specified before the algorithm is run and include population size, selection rate, and operator probabilities. This process is known as offline control and is even considered as an optimization problem in itself. On the other hand, parameter settings or control online is a variation of the algorithm original version. The main idea is to vary the parameters so that the algorithm of interest can provide the best convergence rate and thus may achieve the best performance. In this paper, we propose an adaptive black hole algorithm able to dynamically adapt its population according to solving performance. For that, we use autonomous search which appeared as a new technique that enables the problem solver to control and adapt its own parameters and heuristics during solving in order to be more efficient without the knowledge of an expert user. In order to test this approach, we resolve the set covering problem which is a classical optimization benchmark with many industrial applications such as line balancing production, crew scheduling, service installation, and databases, among several others. We illustrate encouraging experimental results, where the proposed approach is able to reach various global optimums for a well-known instance set from Beasley’s OR-Library, while improving various modern metaheuristics.

This paper presents a new modeling approach for the transmission network expansion planning (TNEP) considering repowering of circuits and integration of small-scale generation. The TNEP consists on determining the new assets that will be needed in the network to meet a forecasted demand. Traditionally, the candidate solutions of this problem only consider the integration of new lines or transformers. In this paper we expand the candidate solutions to include repowering of circuits. Also, the integration of small-scale generation is considered. The TNEP problem is modeled as a mixed integer linear programming problem and solved using the software GAMS. Several tests are performed on the Garver system and IEEE 24 bus test system showing the applicability of the proposed model. Results show that circuit repowering in strategic corridors, combined with the installation of small-scale generation, contributes to a significant reduction of the cost of the TNEP. COL0010477

Designing optimal water supply systems is an important purpose of any urban system that involves relevant installation, operation and maintenance costs. However, achieving the optimal design is known to be a complex task, indeed the corresponding mathematical model for this problem leads to a non-linear and non-convex problem classified as NP-hard. In this paper, we propose using the cuckoo search algorithm which a modern bio-inspired metaheuristic based on the obligate brood parasitic behavior of cuckoo birds. This behavior is combined with the interesting Levy flight, which mimic the exploration of some birds and flies, that move by combining straight flights and ninety degrees turns. The proposed approach results in a fast convergence algorithm able to noticeably reduce the number of objective function evaluations needed to solve this problem.

This paper deals with program optimization, i.e., learning of more efficient programs. The programs we want to improve are Z3 solving strategies. Z3 is a SMT (SAT Modulo Theory) solver which is currently developed by Microsoft Research. We define strategy generators based on evolutionary processes. SMT solving strategies include various aspects that can affect the performance of a SMT solver dramatically. Each of these elements includes a huge amount of options which cannot be exploited without expert knowledge. We define a generic evolutionary algorithm based on genetic programming concepts. This strategy generation process aims at learning better strategies by successive improvements, using rules that can be combined in order to handle both structures and parameters of the strategies. We experiment 7 different strategies generators on 2 SMT logics (QF_LRA,QF_LIA). The results show that the learned strategies improve default strategies available in the solver.

We present Frequency Analyser, an AndroidTM application for measuring the fundamental frequency of a sound wave with very high precision in a frequency range between 100 Hz and 11 kHz. The application allows exporting the data of frequency as a function of time to an ASCII file. Several examples of application to Physics and Engineering teaching experiments are presented. © 2015 Wiley Periodicals, Inc. Comput Appl Eng Educ 23:471–476, 2015; View this article online at wileyonlinelibrary.com/journal/cae; DOI 10.1002/cae.21618